MD Baptiste Maille

MD Marie Wilkin

Matthieu Million

Noémie Resseguier

PhD Frédéric Franceschi

PhD Linda Koutbi-Franceschi

MD Jérôme Hourdain

MD Elisa Martinez

MD Maxime Zabern

MD Christophe Gardella

MD Matthieu Millon

PhD Noémie Rességuier

PhD Herve Dupont-Tissot

PhD Jagmeet P Singh

Professor Jean-Claude Deharo
email: jean-claude.deharo@ap-hm.fr

MD Laurent Fiorina

J-C D Aix

Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study

Keywords: ClinicalTrial.gov NCT04371744 COVID-19, hydroxychloroquine-azythromycine, QTc interval, smartwatch, artificial intelligence

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

The current pandemic due to a new coronavirus -severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) -has so far affected more than 11 million people and caused in excess of half a million deaths. This healthcare emergency made it necessary to explore pharmacologic interventions to treat or prevent the disease. The use of antiviral drugs is one such intervention, but their efficacy remains debated and strategies for their use are evolutive. [START_REF] Cao | A Trial of Lopinavir-Ritonavir in Adults Hospitalized with Severe Covid-19[END_REF] In the absence of definitive proof of effectiveness, the safety profile of any treatment is a crucial question. Cardiac toxicity is one of the main concerns because many of the proposed treatments, including lopinavir/ritonavir, chloroquine/hydroxychloroquine (HCQ), azithromycin (AZM), moxyfloxacin and remdesivir, have the potential to cause proarrhythmia, particularly in the setting of severe forms of SARS-CoV2. [START_REF] Naksuk | Cardiac safety of off-label COVID-19 drug therapy: a review and proposed monitoring protocol[END_REF][START_REF] Roden | Considerations for Drug Interactions on QTc Interval in Exploratory COVID-19 Treatment[END_REF] Monitoring the QT interval and cardiac rhythm are essential for safety considerations regarding the use of these drugs. [START_REF] Roden | Considerations for Drug Interactions on QTc Interval in Exploratory COVID-19 Treatment[END_REF] Some single-lead ECG devices coupled with artificial intelligence (AI) received US Food and Drug Administration clearance for atrial-fibrillation screening, [START_REF] Hannun | Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network[END_REF] but little is known about the feasibility and diagnostic accuracy of their use for QTc assessment. [START_REF] Chung | QTC intervals can be assessed with the AliveCor heart monitor in patients on dofetilide for atrial fibrillation[END_REF] In light of the shortage of resources and widespread use, and in the context of a highly contagious disease, surrogates for 12-lead electrocardiogram (ECG) assessment may be useful, and direct-to-consumer single-lead ECG technology is promising. If previous study had already shown a good agreement between QTc measured on a Smartwatch ECG (SW-ECG) and a 12 leads ECG, [START_REF] Strik | Validating QT-Interval Measurement Using the Apple Watch ECG to Enable Remote Monitoring During the COVID-19 Pandemic[END_REF] little is known about the use of AI for QTc measurement. The Cardiologs Platform (Cardiologs Technologies™, Paris, France) is a cloudbased platform for ECG interpretation powered by a deep neural network algorithm. This algorithm has already been validated, especially for 12 lead-ECG interpretation in emergency departments, [START_REF] Smith | A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation[END_REF] diagnosis of atrial fibrillation, [START_REF] Smith | A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation[END_REF] and for Holter analysis. [START_REF] Fiorina | Abstract 9825: Artificial Intelligence Based Platform Enables Faster Ambulatory Electrocardiogram Analysis With Equivalent Clinical Accuracy Compared to Traditional Solution[END_REF] An analog watch with an in-built singlelead ECG (Withings Move ECG™, Withings, France), linked to the Cardiologs™ AI platform, can send self-recorded ECGs direct for AI analysis.

We performed a study to compare QTc assessed using this algorithm on smartwatch single-lead ECGs (AI-QTc) with QTc measured using conventional 12-lead ECGs in patients with early stage coronavirus disease 2019 (COVID-19) treated with the HCQ-AZM regimen.

METHODS

We identified all consecutive adults (≥18 years) who attended the ambulatory care center of the infectious diseases department of our tertiary referral academic hospital from April 16 to April 24, 2020, for polymerase chain reaction (PCR)-positive SARS-CoV-2 infection. Medical history and current medical status were thoroughly assessed for each patient and the decision to treat with HCQ-AZM was taken by the infectious disease specialist. The study is registered at ClinicalTrials.gov, with the unique identifier NCT04371744. The study was reviewed by the medical research committee of our academic hospital (reference number 2020-52) and approved by the ethical committee (reference number: 2020-030). Signed written informed consent was obtained from all participants.

Cardiac-Rhythm Safety evaluation:

Once the decision to treat was taken, according to local guidelines, Tisdale Score and recent recommendations [START_REF] Roden | Considerations for Drug Interactions on QTc Interval in Exploratory COVID-19 Treatment[END_REF][START_REF] Tisdale | Development and validation of a risk score to predict QT interval prolongation in hospitalized patients[END_REF] a rhythm safety evaluation was systematically performed to assess QTc prolongation risk. Moreover, treatment with HCQ-AZM was not started if the corrected QT interval (QTc; Bazett's formula) was >500 ms, or if the ECG showed patterns suggesting a channelopathy, or if other significant abnormalities (i.e., pathological Q waves, left ventricular hypertrophy, left bundle branch block) were present. The risk-benefit raKo of HCQ-AZM was esKmated by the infecKous disease specialist and agreed upon with the cardiologist, for a range between 460 and 500 ms of QTc.

In addition, any drug with the potential to prolong the QT interval was discontinued or replaced with another drug for the treatment course. Standard blood chemistry was checked, especially serum creatinine and kalemia in the context of SARS-CoV-2, and the treatment was not started when there was hypokalemia with a serum potassium <3 mEq/L, and was discontinued at day 1 if the serum potassium between 3 and 3.5 mEq/l was not normalized.

ECG recording:

All patients who were prescribed HCQ-AZM treatment and had no criteria for overnight stay were considered for inclusion in the study if they had a smartphone and were able and willing to perform repeated SW-ECGs. They received a smartwatch (Withings Move ECG™) and were instructed on how to use it. A 12-lead ECG was then recorded at rest (baseline), with a paper speed of 50 mm/s and an amplitude calibration of 10 mm/mV (MAC® 3500 or MAC® 1600 recorder; GE Healthcare Europe, Freiburg, Germany). As soon as technically possible, an ECG recording was taken with the smartwatch. Patients were then allowed to go home. The drug regimen was as follows: hydroxychloroquine (Plaquenil®; Sanofi Aventis, Paris, France), 200 mg three times per day for 10 days, plus azithromycin (Zithromax®; Pfizer Holding, Paris, France), 500 mg once a day on the first day, then 250 mg once a day for 4 days.

The patients were instructed to transmit a SW-ECG every morning at rest and at any time in case of unusual symptoms, including palpitations or dizziness, until the end of the treatment. As per local guidelines, patients were requested to attend a follow-up visit at the ambulatory care center of the infectious disease department for clinical assessment and a 12-lead ECG on the sixth day (day-6, recommended) and on the last day of therapy (day-10, mandatory). They transmitted a SW-ECG as soon as technically possible after the 12-lead ECG recording at day-6 and day-10 (Figure 1).

Electrocardiogram Interpretation Manual Interpretation

On the standard 12-lead ECG recordings obtained at baseline, day-6 and day-10, the QT interval was measured, as recommended [START_REF] Postema | Accurate electrocardiographic assessment of the QT interval: teach the tangent[END_REF] in the tangent method, in lead II or V5, and corrected using Bazett's formula. In addition, QTc was assessed on lead I using the same method. Additional measurement included heart rate, and any cases of arrhythmia were recorded. PR interval and QRS duration were measured only at baseline and day-10. The interpretation was blindly performed by two cardiologists, (B.M.) and (M.W.). When there was a <30 ms discrepancy between the 2 measures, the mean of the 2 values obtained by the 2 operators was used to compare against the AI-based automatic measurement of QTc. When there was a >30 ms discrepancy between the 2 measures, an additional cardiologist (J-C.D.) performed the measure and this value was used for comparison against the AI-QTc.

The QT interval was manually measured on daily SW-ECGs, using the same method as described above for 12-lead ECGs and compared with the AI-QTc measurements. In addition, manual measurement of the QTc was performed independently by 3 electrophysiologists (B.M., M.W. and L.F.) on the transmitted SW-ECG from day-10. Manual measurements were performed blinded to the AI interpretation, and the mean of the 3 measured values was used for comparison with AI-QTc. In case of SW-ECG transmission due to symptoms, the ECG was interpreted by the same cardiac electrophysiologists. Patients could be contacted in the event of a significant arrhythmia or QTc prolongation >500 ms.

Artificial intelligence Interpretation

AI-QTc were systematically measured from all received SW-ECG. When the 30-s SW-ECG was performed by a patient, it was automatically transmitted to the Cardiologs platform for assessment of the AI-QTc. The AI-QTc was computed as follows: a deep convolutional neural network identified the onset of QRS complexes and the offset of following T waves of all beats in the SW-ECG. The QTc of each beat was computed with the Bazett formula, using the QT and the preceding RR intervals detected by the neural network. Finally, in order to remove extreme and aberrant values, the AI-QTc of the SW-ECG was computed as the median QTc across all beats.

The convolutional neural network consists of a U-net architecture [START_REF] Srivastava | Dropout: a simple way to prevent neural networks from overfitting[END_REF] with 11 convolutional layers and 6 residual blocks. The network takes as input the ECG signal and outputs the onsets and offsets of all detected P, QRS and T waves. The network was trained on 6315 resting ECGs and Holter recordings, with the onsets and offsets of P, QRS and T waves annotated by experts in electrophysiology following standard procedures. No SW-ECG was used during training of the neural network. The SW-ECGs were single lead ECGs lasting 30 s and sampled at 300 Hz, a sampling rate which is similar to the recordings used for training (100 to 500 Hz). The network was implemented in Keras, with a backend in Tensorflow (Google, Mountainview, California), and trained using stochastic gradient descent. Early stopping and dropout regularization were used to avoid overfitting. [START_REF] Srivastava | Dropout: A Simple Way to Prevent Neural Networks from Overfitting[END_REF] In order to ensure that the network performs accurately for multiple and single lead ECGs, leads were sometimes randomly subsampled during training. The performance of the AI-QT measurement was evaluated on the Common Standards for Quantitative Electrocardiography database. [START_REF] Smisek | CSE database: extended annotations and new recommendations for ECG software testing[END_REF] The bias of the QT measurement was -13.7 ms, with a standard deviation of 7.3 ms.

Outcome Measures

The primary outcome was the agreement between the standard 12-lead QTc interval measured manually in lead II or V5 at baseline and day-10, and the AI-QTc measured on the corresponding SW-ECG.

Secondary outcomes included the agreement between the standard 12-lead QTc measured manually in lead II or V5 at the day-6 visit and the AI-QTc on the corresponding SW-ECG; agreement between the 12-lead QTc measured manually in lead I at baseline, day-6 and day-10 and the AI-QTc on the corresponding SW-ECG; agreement between the QTc measured by the cardiologist on the daily SW-ECG and the AI-QTc on the same SW-ECG, at day-10; and a description of QTc behavior during HCQ-AZM therapy and of arrhythmia occurrence as assessed on daily SW-ECGs or on-demand SW-ECGs (i.e. in case of symptoms).

Statistical Analysis

Quantitative variables are presented as means ± standard deviations (SD) and categorical variables as numbers (percentages). Agreement between QTc measurements (between manual QTc measurement on the 12-lead ECGs and AI-QTc, between QTc measured manually on the same SW-ECG and AI-QTc) was assessed using the Bland-Altman method. The mean of the difference (bias) in QTc interval between the 2 methods was calculated, as well as the lower and upper limits of agreement. Agreement between measures was also numerically assessed by estimating the agreement intraclass correlation coefficient (ICC), with its 95% confidence interval (CI). Daily smartwatch QTc were compared with baseline smartwatch QTc by means of a paired t-test. The Benjamini-Hochberg procedure was used for controlling the false positive rate in multiple comparisons.

All analyses were performed using R software, version 3.6.3. All tests were 2-sided, and P<0.05 was considered statistically significant.

RESULTS

Between April 16 and April 24, 2020, 108 consecutive adults with a PCR-positive SARS-CoV-2 infection with no criteria for overnight stay were considered for HCQ-AZM combinaKon therapy initiated at our infectious disease ambulatory care department. The patient flow chart is illustrated in Supplemental figure 1. Of the 85 patients who entered the study, 76 received the drug regimen and were followed with daily SW-ECGs. The epidemiological, clinical, and baseline ECG characteristics of the patient populations are presented in Table 1. In 3 patients (3.9%), serum potassium was between 3 and 3.5 mmol/L at inclusion; they received potassium supplementation, achieving normo-kalemia after 1 day, and could continue the treatment. Ten patients (13.1%) were taking concomitant QTprolonging drugs (antihistamine drugs) and 2(2.6%) were taking beta-blockers. These treatments were all discontinued on the decision of the infectious disease specialist as they were not considered essential. No patient had a significant ECG abnormality or underlying severe cardiac disease. Among patients who received the drug regimen and were followed with daily SW-ECGs, median Tisdale score was 7 (6-7). Respectively 26(34.2%), 50(65.8%) and 0(0%) presented a low, moderate and high risk of prolonging QTc.

Mean follow-up was 10.8±1.2 days. One patient had to be admitted to the conventional ward because of a deterioration in his respiratory function at day 5 of treatment and stayed in hospital for 5 days. No more SW-ECGs were recorded after he was admitted to the hospital, but clinical follow-up and a 12-lead ECG at day 10 were still available. By their own decision, 17(22.4%) patients did not attend the optional day-6 visit and 2(2.6%) did not attend day-10 visit. They were contacted at day-10, and it was confirmed that they did not experience palpitations or syncope. One patient declared a minor cutaneous allergic reaction with pruritus related to the watch strap, which did not prevent the patient from sending daily recordings. With this exception, no patient declared any difficulty in the wearing the smartwatch.

Owing to technical issues in communication between their smartwatch and smartphone, 5/71(6.6%), 5/53(8.6%) and 7/66(9.6%) patients were unable to record a SW-ECG after the 12 lead-ECG during the visit at baseline, day-6, and day-10, respectively. A mean of 9.5±2.1 SW-ECGs were recorded per patient. Fifty (65.8%) patients performed at least 10 SW-ECGs during the 11 days of follow-up, while 6(7.9%) performed fewer than 6 SW-ECGs. No patient reported palpitations, dizziness, or syncope.

The number of patients in whom 12-lead and SW-ECGs could be compared at each visit is detailed in Table 2.

Agreement Between Manual and AI-QTc Intervals

The delay between completion of the standard 12-lead ECG and the SW-ECG was 55.7±141.3 min at baseline, 38.2±129.1 min at day-6, and -2.6±64.7 min at day-10. Table 2 shows the agreement between the manually measured QTc on the 12-lead ECG, the manually measured QTc on lead I of the same tracing, and AI-QTc from the corresponding SW-ECG. When the delay between completion of the 12-lead ECG and the SW-ECG was the shortest, at day-10, the Bland-Altmann diagram (Table 2, Figure 2B) showed the best agreement. Similar results were obtained at day-6 (Table 2, Supplemental Figure 2). The difference between the two measures was <50 ms in 65(98.2%) patients at day-10 and in 51(96.2%) patients at day-6. At baseline, 69(97.2%) patients presented with a lower than 50 ms difference between the two measures. However, the agreement assessed by the Bland-Altmann was lower (Table 2, Figure 2A). Consistently across the three visits, the agreement between the QTc measured in lead I and the AI-QTc showed a tendency to overestimate QTc, with a similar limit of agreement to 12 lead ECG (Table 2, Supplemental figure 3).

Agreement was excellent between the AI-QTc at day 10 and the manual measure of QTc on the same recording (Supplemental figure 4), showing a bias (limits of agreement) of 0 (-18,+17) ms and an ICC agreement (95% CI) of 0.91 (0.85,0.94).

QTc Behavior During Treatment

Figure 3 illustrates daily AI-QTc behavior, during HCQ-AZM therapy. Compared to the baseline value, there was no significant daily prolongation of QTc during treatment (Figure 3A). Similar results were obtained for QTc interval behavior as manually measured on the 12-lead ECGs (Figure 3B). At the individual level, as compared to baseline: 18(23.4%), 5(6.5%) and 7(9.1%) patients had at least one daily SW-QTc prolonged by, respectively, 20-40 ms, 40-60 ms, and >60 ms. In the meanKme, only 29(39.1%), 10(13.6%), and 1(1.4%) were identified by the standard 12-lead ECGs during the followup.

One patient had a AI-QTc >500 ms (i.e. 502 ms) on the second day of treatment and was therefore admitted to the day-hospital. The 12-lead ECG showed a prominent U wave in lead I, along with a low T-wave amplitude. In this patient, baseline 12-lead QTc interval was 457 ms and no AI-QTc was available at baseline because of pairing issues between the smartphone and the smartwatch. The 12lead QTc measured in hospital on day 2 was 471 ms, thus the drug regimen was continued at home.

No QTc value >500 ms was observed on either the subsequent daily AI-QTc or 12-lead ECGs during follow-up.

In the general population, a slight but significant PR prolongation was observed at day-10 (154±25 ms at baseline to 161±23 ms at day-10; P<0.001). There was no significant QRS prolongation from baseline (83±19 ms) to day-10 (86±17 ms) (P=0.3).

Four patients (5.2%) presented with asymptomatic premature ventricular contractions (PVC) on their daily SW-ECGs. All of them had also had such PVC's on their 12 lead-ECG at baseline. No other arrhythmia was identified during follow-up.

DISCUSSION

We observed a fair agreement between the QTc interval duration measured manually on a standard 12-lead ECG and assessed by AI on single-lead smartwatch recordings. This agreement was observed on different sets of tracings obtained at various points throughout the study, provided they were recorded at similar times. In this young population of patients with early stage COVID-19 and mild-tomoderate symptoms, no significant QTc prolongation was observed on daily AI-QTc and no lifethreatening arrhythmias were reported. Whereas one study has already demonstrated the power of a deep learning algorithm for diagnosis of arrhythmia, [START_REF] Hannun | Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network[END_REF] the current is the first to evaluate a deep neural network with SW-ECGs for QT monitoring.

A few studies [START_REF] Cheung | Multilead QT Screening Is Necessary for QT Measurement: Implications for Management of Patients in the COVID-19 Era[END_REF][START_REF] Saleh | Effect of Chloroquine, Hydroxychloroquine, and Azithromycin on the Corrected QT Interval in Patients With SARS-CoV-2 Infection[END_REF] and recent recommendations [START_REF] Varma | HRS/EHRA/APHRS/LAHRS/ACC/AHA worldwide practice update for telehealth and arrhythmia monitoring during and after a pandemic[END_REF] have highlighted the major interest of using connected devices in the context of the potential use of QT-prolonging drugs in COVID-19 therapy.

Previous studies described the utility of various connected ECG recording devices, such as smartphones, [START_REF] Cheung | Multilead QT Screening Is Necessary for QT Measurement: Implications for Management of Patients in the COVID-19 Era[END_REF][START_REF] Garabelli | Comparison of QT Interval Readings in Normal Sinus Rhythm Between a Smartphone Heart Monitor and a 12-Lead ECG for Healthy Volunteers and Inpatients Receiving Sotalol or Dofetilide[END_REF] wearable remote monitoring systems, [START_REF] Castelletti | A wearable remote monitoring system for the identification of subjects with a prolonged QT interval or at risk for drug-induced long QT syndrome[END_REF] smartwatches, [START_REF] Strik | Validating QT-Interval Measurement Using the Apple Watch ECG to Enable Remote Monitoring During the COVID-19 Pandemic[END_REF] and mobile cardiac telemetry [START_REF] Saleh | Effect of Chloroquine, Hydroxychloroquine, and Azithromycin on the Corrected QT Interval in Patients With SARS-CoV-2 Infection[END_REF] for QTc monitoring. Some have also described the use of automatic measurement of the QTc using different automatic algorithms, but not neural network-based AI, embedded on recording devices or proprietary. Our study is the first to confirm the feasibility and accuracy of a neural network-based AI-QTc determination, using ECG independently gathered from direct-to-consumer SW-ECG, in real-life conditions of COVID-19 therapy.

Agreement between 12-lead ECG and a SW-ECG QTc measurements have been shown to be dependent on factors such as ECG tracing quality and T-wave amplitude. [START_REF] Strik | Validating QT-Interval Measurement Using the Apple Watch ECG to Enable Remote Monitoring During the COVID-19 Pandemic[END_REF] Different strategies of improving agreement accuracy have been described. Strik et al. used "T-wave mapping" screening to identify the best smartwatch position and withdrew from their study patients in whom the different smartwatch positions did not allow for adequate measurement. [START_REF] Strik | Validating QT-Interval Measurement Using the Apple Watch ECG to Enable Remote Monitoring During the COVID-19 Pandemic[END_REF] Other investigators excluded patients with poor quality of reference ECG tracing. In our real life study, no patient was excluded based on the shape or quality of the SW-ECG, and only standard recording (i.e. with the watch worn on the wrist) was performed. Moreover, no SW-ECG was used during training of our neural network.

Due to the COVID 19 pandemic emergency situation, there was not enough time to apply such a strategy of specific neural network training. Despite this imperfect emergency protocol, we found a fair agreement with manual-QTc, comparable to that reported in the literature. [START_REF] Strik | Validating QT-Interval Measurement Using the Apple Watch ECG to Enable Remote Monitoring During the COVID-19 Pandemic[END_REF][START_REF] Cheung | Multilead QT Screening Is Necessary for QT Measurement: Implications for Management of Patients in the COVID-19 Era[END_REF][START_REF] Saleh | Effect of Chloroquine, Hydroxychloroquine, and Azithromycin on the Corrected QT Interval in Patients With SARS-CoV-2 Infection[END_REF][START_REF] Varma | HRS/EHRA/APHRS/LAHRS/ACC/AHA worldwide practice update for telehealth and arrhythmia monitoring during and after a pandemic[END_REF][START_REF] Viskin | Inaccurate electrocardiographic interpretation of long QT: the majority of physicians cannot recognize a long QT when they see one[END_REF]. We expect the use of a specific AI trained with SW-ECGs would demonstrate more accurate agreement.

There are numerous sources of potential discrepancy in QTc interval assessment, ranging from interobserver variability and precision of the measurement technology to intrinsic variability of the QTc itself. The discrepancy between manual QTc measurements, even when performed by experts, is wide, ranging from 34 to 80 ms. [START_REF] Viskin | Inaccurate electrocardiographic interpretation of long QT: the majority of physicians cannot recognize a long QT when they see one[END_REF] In addition, QT interval is a highly dynamic parameter, showing well-known circadian variations. Apart from heart rate itself, it is highly sensitive to autonomic nervous system influences [START_REF] Murakawa | Role of sympathovagal interaction in diurnal variation of QT interval[END_REF] and to many pathologic conditions such as ischemia. [START_REF] Puljevic | QT dispersion, daily variations, QT interval adaptation and late potentials as risk markers for ventricular tachycardia[END_REF] Even if we did our best to perform smartwatch and 12-lead ECG recordings as close in time as possible, a significant delay was observed in some cases for various reasons that may reflect real life. In spite of a potential multifactorial QTc variability in measurements, we found acceptable agreement.

As the SW-ECG records a lead equivalent to lead I of the ECG, we were expecting stronger agreement between AI and manual QTc when the measure was performed in lead I. AI-QTc was consistently overestimated compared with lead 1 QTc, but a similar limit of agreement with 12-lead QTc was observed. There is no clear explanation, but we assume that it might be due to the fact that lead I is not optimal for QT measurement for various reasons, including the shape or amplitude of the T wave in lead I, and the QT dispersion on a 12-lead ECG. [START_REF] Baumert | QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electrophysiology[END_REF][START_REF] Cowan | Importance of lead selection in QT interval measurement[END_REF] We believe that AI may compensate for some of these issues by computing a median over all beats, removing the extreme and aberrant values, whereas manual measurement only relies on a few beats.

While HCQ effectiveness in COVID-19 is criticized, [START_REF] Maisonnasse | Hydroxychloroquine use against SARS-CoV-2 infection in non-human primates[END_REF] its use in connective tissue disease is wide [START_REF] Schrezenmeier | Mechanisms of action of hydroxychloroquine and chloroquine: implications for rheumatology[END_REF] and antiviral strategies in COVID-19 still unresolved. Moreover this study was not designed to test the effectiveness of HCQ-AZM in COVID-19 but rather to examine cardiac safety profile of such medication. The use of HCQ-AZM in paKents with early stage COVID-19 with mild-to-moderate symptoms in an ambulatory care center, was not related with significant prolongation of the QTc interval during the drug regimen or significant arrhythmia occurrence. However, HCQ may lead to QT prolongation. HCQ is a derivative of chloroquine and has similarities to quinine, which is a class Ia antiarrhythmic drug that acts as a sodium channel blocker, responsible for prolonging the duration of the action potential, and may therefore prolong the QT interval. However, this effect is expected to be modest. [START_REF] Lakkireddy | Guidance for Cardiac Electrophysiology During the COVID-19 Pandemic from the Heart Rhythm Society COVID-19 Task Force; Electrophysiology Section of the American College of Cardiology; and the Electrocardiography and Arrhythmias Committee of the Council on Clinical Cardiology[END_REF] AZM has low affinity for the hERG channel, [START_REF] Hancox | Azithromycin, cardiovascular risks, QTc interval prolongation, torsade de pointes, and regulatory issues: A narrative review based on the study of case reports[END_REF] and is therefore considered to have a low risk of QT prolongation. However, combining both drugs, particularly when taking into account the increased risk of electrolyte disorders in patients with severe COVID-19, may result in a proarrhythmic effect. Recent publications [START_REF] Bessiere | Assessment of QT Intervals in a Case Series of Patients With Coronavirus Disease 2019 (COVID-19) Infection Treated With Hydroxychloroquine Alone or in Combination With Azithromycin in an Intensive Care Unit[END_REF][START_REF] Chorin | QT interval prolongation and torsade de pointes in patients with COVID-19 treated with hydroxychloroquine/azithromycin[END_REF] evaluating HCQ-AZM treatment in hospitalized patients with COVID-19, with a mean age greater than 60 years, and including severe and critically ill patients with COVID-19, have shown important QTc lengthening in some patients. However, other evaluating lower risk patients, [START_REF] Gasperetti | Arrhythmic safety of hydroxychloroquine in COVID-19 patients from different clinical settings[END_REF] similarly to our study, showed only modest QTc prolongation. This highlighted the necessity of an initial QTc prolongation risk evaluation and a close QTc monitoring, for patients under prolonging QTc drugs. Thus a home-monitoring ECG is of greatest interest, in the context of the required social distancing, because of the COVID-19 pandemic.

Limitations

One of the limitations of our study is the very low number of critical QTc prolongations. This is likely due to our cardiac-rhythm safety criteria for HCQ-AZM therapy. Although the accuracy for monitoring pathologic QTc prolongation remains uncertain, the agreement we found makes it a promising strategy.

Our study also highlights some limitations of SW-ECG follow-up, including willingness and capacity to participate, technical skills, and adequate internet coverage. Twelve patients were not included because of an anticipated poor adherence to the study protocol (either related to patient choice or understanding), and 7 additional patients were withdrawn for technical issues after inclusion. A small number of patients occasionally did not transmit daily SW-ECG data. Finally, as previously mentioned, owing to poor internet coverage in the daycare facility, simultaneous 12-lead ECGs and SW-ECGs were unpredictively not feasible. Despite these issues, participation in the study was good, but different study populations, especially older people, may show more technical difficulties.

Since the determination of QTc duration by an ECG recorder is commonly used, we could have compared the values obtained by AI to the values automatically determined by the ECG recorder.

However, the latter are specific to each ECG recorder and cannot be taken as reference since their accuracy is debated. [START_REF] Diamant | Two automatic QT algorithms compared with manual measurement in identification of long QT syndrome[END_REF][START_REF] Mclaughlin | Comparison of automatic QT measurement techniques in the normal 12 lead electrocardiogram[END_REF] On the other hand, manual measurement is the gold standard for QT measurement [START_REF] Postema | Accurate electrocardiographic assessment of the QT interval: teach the tangent[END_REF], and for this reason we used it for comparison. We see a major benefit of neural network-based QTc determination since it is device-independent and, therefore, generalizable to all ECG recordings having the same characteristics than our SW-ECGs.

CONCLUSIONS

This first "real-world" study evaluating a neural network-based AI-assessed QTc measurement, gathered from direct-to-consumer smartwatches, shows promising results. Despite variability in the QTc interval, fair agreement was observed between AI and 12-lead ECGs. The use of AI-QTc follow-up could potentially avoid life-threatening arrhythmias by stopping treatments that can cause QT prolongation before occurrence of symptoms. This finding also has the potential to lead to future clinical applications in the evaluation of any drug-induced arrhythmogenicity related with QT prolongation, needing close QT interval monitoring.

Figure 1 .

 1 Figure 1. (Central illustration) Schematic of the Study Protocol and Operation of the Cardiologs  System.

Figure 2 .

 2 Figure 2. Comparison of QT-Interval Measurements, using QTc Measured Manually on the 12-Lead ECG and AI-Determined QTc Based on Smartwatch Recordings, at (A) Baseline and (B) Day-10. The Bland-Altman Method was Used for Analysis of Measurement Agreement.

Figure 3 .

 3 Figure 3. QTc Behavior With Hydroxychloroquine-Azithromycin Combination Treatment in a Daycare Population with COVID-19.

Figure 3

 3 Figure 3

Table 1 . Patient Characteristics

 1 ACE denotes angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; HCQ-AZM, hydroxychloroquine plus azithromycin combination; and SW-ECG, smartwatch electrocardiogram.

	Characteristic	Overall Population	Patients on HCQ-AZM
		(n=85)	combination and daily
			SW-ECG
			(n=76)
	Male sex	30(35.3)	27(35.5)
	Mean age, y	38.3±12.2	38.2±12.4
	≥65 y	0(0)	0(0)
	Body mass index, kg/m²	26.4±5.4	26.6±5.7
	Cardiovascular risk factor
	Hypertension	4(4.7)	4(5.3)
	Diabetes mellitus	2(2.4)	2(2.6)
	Active smoker	19(22.3)	17(22.4)
	Clinical setting
	Oxygen saturation <94%	1(1.2)	1(1.3)
	Systolic blood pressure, mmHg	128±16	129±16
	Fever (>38 °C)	7(8.2)	6(7.9)
	Cardiovascular treatment
	ACE inhibitor/ARB	2(2.4)	2(2.6)
	Beta-blocker	2(2.4)	2(2.6)
	For continuous variables, values are mean±standard deviation; for categorical variables, n(%) is
	shown.


Weijie Lan
email: weijie.lan@inrae.fr

Catherine M G C Renard
email: catherine.renard@inrae.fr

Benoit Jaillais
email: benoit.jaillais@inrae.fr

Alexandra Buergy
email: alexandra.burgy@inrae.fr

Alexandre Leca
email: alexandre.leca@inrae.fr

Songchao Chen
email: songchao.chen@inrae.fr

Sylvie Bureau
email: sylvie.bureau@inrae.fr

Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing

Keywords: Malus x domestica Borkh., Mid infrared spectroscopy, PLS models, Direct standardization, Discriminant analysis

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Apple puree is one of the major industrially processed fruit products (over 0.3 million tons consumed per year in France and the world market value about 2000 million USD annually) [START_REF] Franceagrimer | ) and by Region -Global Forecast till 2023[END_REF]Market Research Future, 2019), and can be used as the basic ingredient of jams, preserves or compotes [START_REF] Defernez | Use of infrared spectroscopy and chemometrics for the authentication of fruit purees[END_REF]. The quality of apple purees is systematically influenced by both raw material characteristics [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF][START_REF] Lan | Visible, near-and mid-infrared spectroscopy coupled with an innovative chemometric strategy to control apple puree quality[END_REF]Lan, Jaillais, Leca, Renard, & Bureau, 2020;[START_REF] Rembiałkowska | Influence of Processing on Bioactive Substances Content and Antioxidant Properties of Apple Purée from Organic and Conventional Production in Poland[END_REF] and cooking strategies (heating, grinding speed and refining levels etc.) [START_REF] Espinosa | Effect of processing on rheological, structural and sensory properties of apple puree[END_REF][START_REF] Oszmiański | Influence of apple purée preparation and storage on polyphenol contents and antioxidant activity[END_REF][START_REF] Picouet | Minimal processing of a Granny Smith apple purée by microwave heating[END_REF]. In practical apple processing, industrial manufactures have to face the variability and heterogeneity of raw apples, optimize their processing strategies to maintain the sustainable and expected quality level of final puree products. Thus, developing rapid, efficient and integrated methods is needed to guide suitable fruit processing procedures, even to design innovative foods by using the raw material variability, and to reduce fruit wastes all along the processing chain.

Mid infrared spectroscopy (MIRS) is one of the main candidates for both the quantification and qualification of agricultural commodities and processed food [START_REF] Bureau | Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review[END_REF][START_REF] Downey | Food and food ingredient authentication by mid-infrared spectroscopy and chemometrics[END_REF]. Although MIRS presents a relatively lower ability for quantification than that of chromatography or mass spectrometry, it has the advantage of a rapid data acquisition and can provide informative fundamental vibrations of molecular bonds [START_REF] Karoui | Spectroscopic techniques coupled with chemometric tools for structure and texture determinations in dairy products[END_REF]. It does require a minimal sample preparation as the measurement must be done on homogeneous samples as liquid, puree or powder due to the very low penetration of radiation into the samples. Direct MIR characterizations of raw and processed fruits have shown considerable aptitudes to evaluate soluble solids content (SSC), dry matter content (DMC), titratable acidity (TA), some individual sugars, organic acids, rheological (viscosity and viscoelasticity) and structural (particle averaged size and volume) properties [START_REF] Ayvaz | Monitoring multicomponent quality traits in tomato juice using portable mid-infrared (MIR) spectroscopy and multivariate analysis[END_REF]Lan, Renard, Jaillais, Leca, & Bureau, 2020). These studied parameters are related to the taste (SSC, DMC, TA, malic acid), the texture (viscosity, viscoelasticity, particles and cell wall contents) and the basic nutrients (fructose, sucrose and glucose) impacting in a large amount puree quality [START_REF] Bureau | Effect of Sample Preparation on the Measurement of Sugars, Organic Acids, and Polyphenols in Apple Fruit by Mid-infrared Spectroscopy[END_REF][START_REF] Espinosa-Muñoz | Structural parameters that determine the rheological properties of apple puree[END_REF][START_REF] Fügel | Quality and authenticity control of fruit purées, fruit preparations and jams-a review[END_REF].

Currently, our interest is to determine the possibility of using this technique to anticipate the characteristics of processed materials from the data acquired on homogenized raw fruit. According to our previous studies, strong correlations of spectral, chemical and textural properties between raw apples and the corresponding purees have been pointed out (Lan, Jaillais, Leca, Renard, & Bureau, 2020;Lan, Renard, Jaillais, Leca, & Bureau, 2020). Based on that, the quality of final processed purees could be predicted by the infrared spectral information acquired on raw apples using partial least square (PLS) regression (Lan, Jaillais, Leca, Renard, & Bureau, 2020). The main drawback of this strategy is the need, for modelling, to systematically acquire the corresponding spectra on both raw and processed materials with a large number of conditions representative of the variability, giving often only rough assessments. In addition, the internal correlations of quality traits during puree processing were only confirmed under one of the most commonly used processing conditions (Lan, Jaillais, Leca, Renard, & Bureau, 2020).

Direct standardization (DS) is a simple and efficient chemometric tool for the calibration transfer between spectral measurements or between two different sets of conditions, such as the spectral calibration from the off-line to on-line spectra of olive fruits [START_REF] Salguero-Chaparro | Calibration transfer of intact olive NIR spectra between a pre-dispersive instrument and a portable spectrometer[END_REF]. As far as we know, this method has never been considered to bridge the spectral variations along the fruit processing chain. Our interest of this method is thus to find the spectra relationships of all processed purees and their corresponding spectral information acquired on homogenized apples, and to calculate the reconstructed processed puree spectra according to their relative spectral information acquired on apples by DS, taking into account both the variability of raw materials and of commonly used processing conditions. If so, the predictive models of puree quality traits (biochemical and physical) using their reconstructed spectra dataset open the possibility to i) predict the properties of processed apples based on the prior information of raw materials; ii) provide sustainable and precise processing strategies to estimate the quality potential of final products, and iii) to compare in silico the prediction results of different processing approaches to better control the quality of fruit products.

Accordingly, this work aimed to assess the potential of MIRS to: i) detect the variability of the cooked apple purees according to the pre-and post-harvest conditions (fruit thinning and storage periods) and the main processing conditions (heating temperature and grinding speed); ii) calculate reconstructed spectra of purees taking into account the variability of raw fruits and processing conditions; and iii) characterize and anticipate the biochemical (SSC, DMC, TA, individual sugars and malic acid), rheological (viscosity and viscoelasticity) and textural (particle size and volume) properties of the processed purees.

Materials and methods

Apples and purees

Apples

The experiment was conducted on the cultivar 'Golden Delicious' in 2017 and 2019.

All apples were harvested at commercial maturity from La Pugère experimental orchard (Mallemort, Bouches du Rhône, France) (Figure 1).

-In 2017, half of the 'Golden Delicious' apples were subjected to a commercial chemical fruit thinning (Th+) with standard fruit load (50-100 fruits/tree), the other half was not thinned (Th-), resulting in a high fruit load (150-200 fruits/tree). After harvesting, apples were processed into purees the day after harvest (T0), and after one (T1), three (T3) and six months (T6) of cold storage at 4°C.

-In 2019, the commercially thinned 'Golden Delicious' apples (Th+) were stored for up to one month (T1) at 4 °C until starch regression, then processed into purees under different processing conditions.

Puree processing

Before processing, and for each condition, around 2 kg apples were homogenized at 11000 rpm with an Ultraturrax T-25 (IKA, Labortechnik, GmbH, Staufen, Germany) as raw apple homogenates. Three batches of apples (3 kg each) were used to produce three puree lots for each condition. After sorting and washing, Golden Delicious apples were cored and cut in 8 portions, then processed in a multi-functional processing system (Roboqbo, Qb8-3, Bentivoglio, Italy) by different conditions (Figure 1):

-In 2017, all apple groups (2 thinning practices × 4 storage periods) were cooked with a standard Hot Break recipe of 95°C for 5 min at a 1500 rpm grinding speed, then cooled down to 65°C while maintaining the grinding speed. Finally, 24 different cooked purees (2 thinning practices × 4 storage periods × 3 lots) were prepared.

-In 2019, each apple group was processed with three different heating temperatures of 70°C, 83°C and 95°C for 30 min, while ground at three speed levels of 300 rpm, 1000 rpm and 3000 rpm, respectively. Totally, 27 different cooked purees (3 heating temperatures × 3 grinding speeds × 3 lots) were prepared.

Finally, all cooked purees were conditioned in two hermetically sealing plastic bags: one was cooled at room temperature (22.5 °C) before the next-day measurements of rheological, structural and some biochemical (SSC, TA, fructose, sucrose, glucose and malic acid) properties. And the other one was freeze-dried (FD) and stored at -20 °C for the determination of the content of cell wall, which are known to be a major contributor of rheological properties of apple purees [START_REF] Espinosa-Muñoz | Structural parameters that determine the rheological properties of apple puree[END_REF].

Determination of puree quality traits

Rheological and structural characterizations on cooked purees

The cooked puree rheological measurements were carried out using a Physica MCR-301 controlled stress rheometer (Anton Paar, Graz, Austria) and a 6-vane geometry (FL100/6W) with a gap of 3.46 mm, at 22.5 °C. The flow curves were performed after a pre-shearing period of 1 minute at a shear rate of 50 s -1 , followed by 5 minutes at rest. The viscosity was then measured at a controlled shear rate range of [10; 250] s -1 on a logarithmic ramp. The values of viscosity at 50 s -1 and 100 s -1 (η50 and η100 respectively) were kept as final indicators of the puree viscosity linked to sensory characteristics during consumption [START_REF] Engelen | Oral Processing and Texture Perception[END_REF]. Amplitude Sweep (AS) tests were performed at an angular frequency of 10 rad./s in the deformation range of [0.01; 100] %, in order to determine the linear viscoelastic range of the purees and the yield stress, defined as the crossing point between the storage modulus (G') and the loss modulus (G'') curves.

Cooked purees were diluted in distilled water to separate particles, stained with calcofluor (0.1 g/L) and highlighted with a 365 nm UV lamp [START_REF] Soukup | Selected Simple Methods of Plant Cell Wall Histochemistry and Staining for Light Microscopy[END_REF]. The particle sizes averaged over volume d(4:3) (de Brouckere mean) and over surface area d(3:2) (Sauter mean) were measured by a laser granulometer [START_REF] Rawle | Basic of principles of particle-size analysis[END_REF] (Mastersizer 2000, Malvern Instruments, Malvern, UK).

Biochemical analyses on cooked purees

SSC was determined with a digital refractometer (PR-101 ATAGO, Norfolk, VA, USA) and expressed in °Brix at 20 °C. TA was determined by titration up to pH 8.1 with 0.1 mol/L NaOH and expressed in mmol H + /kg of fresh weight (FW) using an autotitrator (Methrom, Herisau, Switzerland). Individual sugars and malic acid were quantified using colorimetric enzymatic kits, according to the manufacturer's instructions (R-biopharm, Darmstadt, Germany). The content of glucose, fructose, sucrose and malic acid were expressed in g/kg FW. These measurements were performed with a SAFAS flx-Xenius XM spectrofluorimeter (SAFAS, Monaco) at 570 nm for the sugars and 450 nm for malic acid. The DMC was estimated from the weight of freeze-dried samples upon reaching a constant weight (freeze-drier, 5 days). Cell wall materials of purees were isolated using the alcohol insoluble solids method proposed by [START_REF] Renard | Variability in cell wall preparations: quantification and comparison of common methods[END_REF] and the cell wall contents (AIS contents) were expressed in both FW and dry matter weight (DW).

Spectrum acquisition on raw apple homogenates and cooked purees

The MIR spectra were acquired at 23 °C using a Tensor 27 FTIR spectrometer (Bruker Optics®, Wissembourg, France) equipped with a horizontal attenuated total reflectance (ATR) sampling accessory and a deuterated triglycine sulphate (DTGS) detector. The samples were placed at the surface of a zinc selenide crystal (ATR-ZnSe) with six internal reflections. Spectra with 32 scans for ATR-ZnSe were collected from 4000 cm -1 to 650 cm -1 with a 4 cm -1 resolution and were corrected against the background spectrum of air. Three replications of spectral measurement were done for each homogenized raw apples and each cooked apple puree.

The whole spectral dataset of MIR is described in Figure S1. It included i) 81 spectra of raw apple homogenates, of which 72 spectra acquired in 2017 (3 apple batches × 2 fruit thinning conditions × 4 storage times × 3 spectral replicates) and 9 spectra acquired in 2019 (3 apple batches × 3 spectral replicates); and ii) 153 spectra of cooked apple purees, containing 72 spectra acquired in 2017 (2 fruit thinning conditions × 4 storage times × 3 processing lots × 3 spectral replicates) and 81 spectra acquired in 2019 (3 heating temperatures × 3 grinding levels × 3 processing lots × 3 spectral replicates).

Statistical analyses of reference data

The reference data of cooked purees processed in 2017 and 2019 are presented as the mean values and the data dispersion within our experimental dataset expressed as standard deviation values (SD). After the Shapiro-Wilk tests, the references data of processed purees affected by fruit thinning and storage times were normal distributed (α=0.05), but not for the dataset of heating temperature and grinding effects during puree processing. Thus, analysis of variance (ANOVA) was carried out to determine the significant differences of cooked purees due to fruit thinning and storage times applied on raw apples (Table S1) using XLSTAT (version 2018.5.52037, Addinsoft SARL, Paris, France) data analysis toolbox. Kruskal-Wallis tests were performed to evaluate the effects of heating temperature and grinding levels during puree processing (Table S2).

Spectra transferred by direct standardization (DS)

In this study, DS was used to find the relationship between the spectra matrices of all cooked purees ( ) and their corresponding spectra of raw apple homogenates ( ), taking into account the effects of raw material variability and processing conditions.

The DS transfer works were performed in R software (version 4.0.2) (R Core Team, 2019) following a previous report [START_REF] Ji | Accounting for the effects of water and the environment on proximally sensed vis-NIR soil spectra and their calibrations[END_REF]:

= + (1)
where B is the transfer matrix (λ × λ) presenting the variations in both and , E is the residual matrix used to correct the baseline difference.

, and E matrices have the same size n × λ, where n presents the numbers of transfer spectra and λ is the number of wavenumbers between 1800 and 900 cm -1 .

First, to compute the transfer B and error E matrices, the whole MIR spectral dataset ( and ) was divided into: the calibration matrices, presenting the first two batches of raw apple homogenates ( ) and the first two lots of cooked purees ( ), and the validation matrices with the third batch of raw apple homogenates ( ) and the third lot of cooked purees ( ) (Figure S1).

In a second step, DS was performed separately on the calibration matrices of raw apple homogenates ( ) and cooked purees ( ) in 2017 ( and ) and 2019 ( and ) (Figures S1 and2):

-the calibration matrices of apples ( ) and purees ( ) were processed to obtain the and related to the effects of raw materials on the processed purees as follows:

= + (2)
Both ( ) and ( ) have the same size n × λ , where n = 48; (2 thinning practices × 4 storage periods × 2 apple batches/ puree lots × 3 spectral replicates.

-the calibration matrices of apples ( ) and purees ( ) were performed for each puree processing condition, as follows:

( ) = + (3)
where i from 1 to 9, corresponding to 9 different processing conditions (3 heating temperatures × 3 grinding speeds). To each spectral replicate of corresponds nine spectra according to each processing condition ( ). All the spectra of matrix corresponding to the same processing conditions were gathered in a specific matrix ( ) . The size of this matrix (one for each processing condition) is equal to that of raw apple homogenates (n = 6 spectra (2 apple batches × 3 spectral replicates) × λ).

Thirdly, once all the transfer B ( and ) and error E ( and ) matrices were computed, they were used to calculate the cooked puree reconstructed calibration and validation spectra matrices, as follows (Figure 2):

= + (4) = + (5) = + (6) = + (7)
Finally, the reconstructed calibration and validation spectral matrices of cooked puree, ( + ) and ( + ) of the two years (2017 and 2019) were obtained with the same sizes of the real spectral matrices of cooked puree, and , for the further multivariate regressions.

Multivariate regression

Spectral pre-processing and multivariate data analysis were performed with Matlab 7.5 (Mathworks Inc. Natick, MA, USA) software using the SAISIR package [START_REF] Cordella | SAISIR: A new general chemometric toolbox[END_REF]. After pretests of several pre-processing treatments (baseline correction, standard normal variate (SNV) and a derivative transform calculation using Savitzky-Golay method (window size = 11, 21, 31) applied on several different spectral regions, the best results of prediction and discrimination were obtained on the range 1800-900 cm -1 , which has been already highlighted (Lan, Renard, Jaillais, Leca, & Bureau, 2020). Principal Component Analysis (PCA) and Factorial Discriminant Analysis (FDA) were applied on SNV pre-treated spectra of cooked purees to detect their differences related to the variability of both, raw apples and processing conditions.

The specificity and sensitivity values of FDA discriminations were calculated by the already reported method of [START_REF] Nargis | Raman spectroscopy of blood plasma samples from breast cancer patients at different stages[END_REF].

PLS models were developed using the SNV pre-processed puree spectra (1800-900 cm -1 ) of the calibration set and the DS transferred spectra of purees ( ) , corresponding to the same reference dataset. The two calibration matrices of cooked purees included a total of 102 spectra (48 spectra in 2017: 2 thinning practices × 4 storage periods × 2 lots × 3 spectral replicates, and 54 spectra in 2019: 3 heating temperatures × 3 grinding speeds × 2 lots × 3 spectral replicates). Then, the developed PLS models were applied on their corresponding validation spectra sets of and , with a total of 51 spectra in 2017: 2 thinning practices × 4 storage periods × 1 lot × 3 spectral replicates and in 2019: 3 heating temperatures × 3 grinding speeds × 1 lot × 3 spectral replicates. PLS model performance was assessed using the determination coefficient of calibration (Rc 2 ) and validation (Rv 2 ), the root-mean-square error of validation (RMSEV), the number of latent variables for calibration (LVs), the residual predictive deviation of validation set (RPD), as described by [START_REF] Nicolai | Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review[END_REF]. The linkable spectral regions of the acceptable PLS models presenting RPD values higher than 2.5 [START_REF] Nicolai | Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review[END_REF] were displayed based on their β-coefficients (Tables 1 and2).

Results and discussion

3.1 Variability of cooked purees based on their MIR Spectra

Variability induced by the raw materials

According to ANOVA (F-values), fruit thinning applied on apples during their growth in orchard resulted in a significant variation (p < 0.001) of viscosity (ŋ50 and ŋ100), viscoelasticity (G', G'', yield stress), particle sizes (d4:3 and d3:2) and biochemical compositions (DMC, SSC, TA, pH, malic acid, sucrose, fructose and AIS) of the cooked purees. Particularly, the impact of thinning on the viscosity, DMC and SSC of purees was higher than the effect of post-harvest storage at 4°C (Table S1).

Purees processed from thinned apples (Th+) had higher viscosity values (η50 and η100)

and bigger particle sizes (d4:3) than those from the non-thinned apples (Th-), observed after the three months of cold storage (T3 and T6) [START_REF] Buergy | Pectin modifications in raw fruits alter texture of plant cell dispersions[END_REF]. Moreover, an intensive decrease of average particle sizes (d 4:3) was observed in the purees cooked with the apples stored one month at 4°C (T1) for both 'thinning' (Th+) and 'non-thinning' (Th-) treatments. PCA applied on the spectra of cooked purees in 2017 showed a good ability to detect the effects of treatments applied on raw apples (fruit thinning and storage periods) (Figure 3a and3b). The effect of thinning on the first principal component (PC1 90.1%) was much higher than that of storage on the second principal component (PC2 6.9%), which was in line with our previous results (Lan, Renard, Jaillais, Leca, & Bureau, 2020). In addition, the increase of the band at 1022 cm -1 and the decrease of the bands at 1061-1065 cm -1 , attributed to sucrose and fructose respectively [START_REF] Bureau | Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review[END_REF], were the major contributors of the observed discriminations on the two PCs (Figure 3c and3d).

Variability induced by processing conditions

The different grinding speeds affected significantly (p < 0.05) the viscosity (η50 and η100), viscoelasticity (G' and G'') and particle size (d4:3 and d3:2) of the cooked purees (Table S2). Particularly, the increase of grinding speed significantly (p < 0.001) decreased viscosity (η50 and η100), viscoelasticity (yield stress, G' and G'') and particle sizes (d4:3) which was observed at each tested temperature. From macroscopic images of purees (data not shown), the larger particles disappeared with increasing grinding speeds, which was enough to cause a decrease in the puree viscosity [START_REF] Espinosa-Muñoz | Structural parameters that determine the rheological properties of apple puree[END_REF]. Inversely, the increasing heating temperatures induced no significant (p > 0.05) changes of puree viscosity (η50 and η100)

and viscoelasticity (yield stress, G' and G''). The highest heating temperature (95℃) resulted in a significant (p < 0.05) increase of DMC and SSC and a decrease of TA and malic acid. Consequently, the changes of grinding speed during the puree processing significantly modified the structural properties and viscoelastic behaviors of purees, whereas heating temperature affected strongly the biochemical composition of purees.

FDA performed on the cooked puree spectra in 2019 successfully classified the processing changes induced at the different heating temperatures (Figure 4a) and grinding speeds (Figure 4b). The samples cooked at 95 ℃ were well-separated from the other two conditions (4 factors, 100% of sensitivity and specificity in Table S3a), according to the first factorial component (F1) (Figure 4a). The specific bands at 1745 cm -1 and 1539 cm -1 were attributed to the increase of soluble pectins, probably in relationship with their solubilization in puree serum from apple cell walls, enhanced with the increasing heating temperature [START_REF] Liu | Modification of apple, beet and kiwifruit cell walls by boiling in acid conditions: Common and specific responses[END_REF]. Moreover, the negative peaks at 1057 cm -1 and 998 cm -1 could be due to the hydrolysis of sucrose during thermal processing, thus resulting in the increase of fructose (1022 cm -1 ) and glucose (1107 cm -1 ) contents.

The three different grinding levels could be discriminated according to the first two factorial components (F1 and F2) (Figure 4b), especially for the highest grinding speed at 3000 rpm ('G3' in Figure 4b) (4 factors, over 85.19% of specificity and sensitivity in Table S3b). The intensive negative spectral peaks at 1558, 1539-1541, 1508 and 1458 cm -1 along both the two discriminant factors (F1 and F2 in Figure 4d and4e)

were all located in the region between 1450 cm -1 and 1600 cm -1 , which has been already attributed to the changes of particle size and rheological behavior after apple puree mechanical refining in a previous experiment (Lan, Renard, Jaillais, Leca, & Bureau, 2020). These peaks indicated the decrease of particle size (d4:3 and d3:2) and viscosity of purees with increasing grinding speeds, which was in line with our reference measurements (Table S2) and [START_REF] Espinosa | Effect of processing on rheological, structural and sensory properties of apple puree[END_REF].

Briefly, MIR technique could detect several kinds of variability sources such as thinning practices during fruit cultivation, cold storage and processing conditions (temperature and grinding) in the cooked purees. In addition, the spectral region 1450-1750 cm -1 was validated as being a reliable analytical signal of processing linked to the textural and rheological changes in purees.

Prediction of quality traits of cooked purees by MIRS

For all developed PLS models, as expected the decreases of determination coefficients between the calibration set (Rc 2 ) and the validation set (Rv 2 ) were observed in Table 1. According to RPD values over 2.5 [START_REF] Nicolai | Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review[END_REF], prediction was acceptable to good (RPD from 2.6 to 3.3) for viscosity (η50 and η100), average particle sizes (d4:3), SSC, TA, pH values and malic acid were content in cooked purees by MIRS, taking into account a large variability of raw apple materials and processing conditions.

Apparent puree viscosity at a shear rate value of 50 s -1 (η50), which has been described to be the highly correlated with the in-mouth texture perception of fluid food [START_REF] Chen | Food oral processing: fundamentals of eating and sensory perception[END_REF], could be predicted by MIRS with a Rv 2 of 0.87 and a RPD of 3.2. MIR prediction of apparent puree viscosity at a single shear rate value of 100 s -1 (η100) (Rv 2 = 0.85, RPD= 3.0) observed here were much better than its prediction by NIRS (RPD = 1.3) (Lan, Jaillais, Leca, Renard, & Bureau, 2020), These results evidenced the possibility of MIRS to estimate puree viscosity. For the two apparent puree viscosity values measured at η50 and η100, the main wavenumber regions at 1718-1730 cm -1 and 1618-1678 cm -1 were still observed in our previous work (Lan, Renard, Jaillais, Leca, & Bureau, 2020). This could validate the application of MIRS to predict puree viscosity by taking into account not only the raw fruit variability but also the complex effects of processing conditions. However, the predictions (Rv 2 < 0.81, RPD < 2.1) of the viscoelastic parameters of purees (G', G'' and yield stress) were not precise enough to estimate the viscoelastic behaviors and the moment when puree starts to flow.

Indeed, heating and grinding affected puree viscoelasticity (SD values of 2362 Pa for G', 595 Pa for G'', 27.1 Pa for yield stress) and resulted in more than twice higher variations of these parameters than those induced by thinning and cold storage on raw materials (SD values of 1001 Pa for G', 234 Pa for G'' and 12.9 Pa for yield stress).

These new prediction accuracies of the viscoelastic parameters of purees (G', G'' and yield stress) were not as good as our previous ones by MIRS (Lan, Renard, Jaillais, Leca, & Bureau, 2020), but could be more robust to be considered for future applications. MIR coupled with the linear regression (PLS) showed a good performance (Rv 2 = 0.87, RPD= 3.1) to evaluate the volume average particle size of purees (d4:3), but not the surface average particle size (d3:2). Particularly, the most informative wavenumbers to evaluate puree particle size at 1701-1713 cm -1 , 1655-1668 cm -1 and 1537-1541 cm -1 have been already observed previously to predict puree viscosity, to discriminate the purees prepared with different grinding speeds (mentioned in part 3.1)

and with different refining levels (Lan, Renard, Jaillais, Leca, & Bureau, 2020). Such good prediction of puree average particle size (d4:3) could not come from internal correlations with puree composition such as SSC, DMC or AIS contents because of their poor correlation (R 2 < 0.48), but probably some specific signals needing to be identified and confirmed. Moreover, we confirmed here the impossibility to predict the cell wall content directly in puree by MIRS, without any preparation such as freezedrying (Lan, Renard, Jaillais, Leca, & Bureau, 2020)

A good prediction of global puree quality traits, SSC (RPD= 3.1) and DMC (RPD= 2.9), was obtained with 5 LVs (Table 1). The variation of SSC and DMC in purees were highly correlated (R 2 = 0.76), which explained the good estimations of these two parameters. Their respective fingerprint wavenumbers of SSC and DMC prediction were similar and detected at 996-1001 cm -1 , 1048-1057 cm -1 and 1109-1112 cm -1 , corresponding to the variations of sucrose, fructose and glucose in purees [START_REF] Bureau | Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review[END_REF]. Moreover, the prediction of TA was excellent (RPD = 3.2), with a limited RMSEV of 7.6 mmol H + /kg FW. The typical wavenumber region at 1709-1720 cm -1 in TA prediction has already been attributed to the C=O vibration of acid group [START_REF] Bureau | Determination of the Composition in Sugars and Organic Acids in Peach Using Mid Infrared Spectroscopy: Comparison of Prediction Results According to Data Sets and Different Reference Methods[END_REF][START_REF] Clark | Fast determination by Fourier-transform infrared spectroscopy of sugar-acid composition of citrus juices for determination of industry maturity standards[END_REF]. Depending on the good correlations between the different contributors of apple acidity (R 2 = 0.81 between TA and malic acid, R 2 = 0.76 between TA and pH),

MIRS provided an acceptable prediction of pH (Rv 2 = 0.83 and RPD = 2.5) and malic acid content (Rv 2 = 0.85 and RPD= 2.7). Despite the similar typical fingerprints observed in the β-coefficients of PLS models of malic acid and pH, the relative lower RPD values and Rv 2 of pH compared to malic acid were probably due to the low pH variation. Concerning the main individual sugars, acceptable prediction was obtained only for fructose (RPD = 2.6), but neither for sucrose (RPD= 1.3) nor for glucose (RPD = 1.5), which was in line with our previous results (Lan, Renard, Jaillais, Leca, & Bureau, 2020). The lower concentration of glucose (10.4-25.4 g/kg FW) than the other individual sugars (34.9-98.7 g/kg FW of fructose, 39.1-118.5 g/kg FW of sucrose) led to its worse prediction by MIR results. A higher internal biochemical correlation between the major compounds (SSC, TA) and fructose (R 2 = 0.79 for SSC and fructose, R 2 = 0.76 for TA and fructose) in apple purees might explain the better prediction of fructose than the one of sucrose (R 2 = 0.58 for SSC and sucrose, R 2 = 0.44 for sucrose and TA).

Briefly, MIR technique can provide a simultaneous and robust estimation of biochemical compositions (dry matter, soluble solids, titratable acidity, pH, malic acid and fructose), rheological behaviors (viscosity at η50 and η100) and particle size (d4:3) of apple purees, taking into account the large variability along the apple puree production chain (agricultural practices, post-harvest storage and processing conditions).

Reconstructed spectra for prediction of puree quality traits

In this part, MIR prediction models were developed using the reconstructed spectra of the calibration set of cooked purees ( ), only done for the well-predicted parameters mentioned in part 3.2, which were η50 and η100, d4:3, SSC, DMC, TA, pH, malic acid and fructose. Then, these models were applied on the validation reconstructed spectra of the cooked purees ( ).

Overall, based on the PLS regression applied on the puree reconstructed spectra, acceptable predictions were obtained (RPD > 2.5) for rheological (η50, η100), structural (d4:3) and global biochemical (SSC, DMC, TA) parameters (Table 2). In contrast, predictions appeared not acceptable for malic acid (RPD = 2.3), fructose (RPD = 1.7)

and pH (RPD = 2.1). Compared to the previous prediction from the real puree spectra (Table 1), lower Rv 2 and higher LVs have been generally obtained for all parameters giving some lower prediction performance (Table 2). Particularly, the use of the reconstructed spectra showed a good ability to predict puree viscosity parameters (η50 and η100) with Rv 2 > 0.82, RPD > 2.5 and prediction errors (RMSEV) of 0.21 Pa.s and 0.10 Pa.s for η50 and η100, respectively. These results were close to those from the real spectra of purees (Table 1). The fingerprint wavenumbers used in the PLS models were similar for both reconstructed and real spectra, mainly 1718-1734 cm -1 , 1616-1336 cm - 1 and 1547-1553 cm -1 as described in Part 3.2. Although a relative lower Rv 2 and RPD (Rv 2 = 0.84 and RPD = 2.6) were obtained for particle size (d4:3) compared to the results on the real puree spectra (Table 1), the consistent fingerprints were highly related to the puree texture such as 1701-1715 cm -1 , 1537-1541 cm -1 and 1101-1107 cm -1 . These prediction performances revealed for the first time the possibility to evaluate the variation of averaged particle sizes in the cooked purees based on the MIR information of the corresponding raw apple homogenates. Considering the other global quality parameters, acceptable predictions were obtained for SSC (Rv 2 = 0.85 and RPD = 2.8)

and DMC (Rv 2 = 0.84 and RPD = 2.6) contents. The specific wavenumbers in the ranges 997-1001 cm -1 and 1048-1057 cm -1 for sucrose and in the ranges 1009-1112 cm -1 for fructose mainly contributed to the PLS models for both reconstructed and real spectra.

These ranges have been already mentioned to be linked to these sugars [START_REF] Bureau | Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review[END_REF], which are the main ones in apples. For acidity, the reconstructed spectra gave an excellent prediction of TA (Rv 2 = 0.86 and RPD = 2.9), using the spectral regions between 1709-1720 cm -1 of the typical C=O absorption [START_REF] Clark | Fast determination by Fourier-transform infrared spectroscopy of sugar-acid composition of citrus juices for determination of industry maturity standards[END_REF]. Consequently, PLS applied on the reconstructed MIR spectra calculated from the spectra of raw apple homogenates showed the possibility to directly predict the viscosity, averaged particle sizes, SSC, DMC and TA of cooked purees.

Several initial attempts have been tested to monitor the quality of cooked food from infrared information of their raw materials with the objectives to predict the texture of cooked poultry pectoralis major muscles [START_REF] Meullenet | Prediction of the texture of cooked poultry pectoralis major muscles by near-infrared reflectance analysis of raw meat[END_REF], of cooked rice [START_REF] Windham | Prediction of cooked rice texture quality using near-infrared reflectance analysis of whole-grain milled samples[END_REF] and of apple purees (Lan, Jaillais, Leca, Renard, & Bureau, 2020). In these studies, the spectra matrix of the raw materials and the reference data of the corresponding processed materials were used to calibrate models. The predictions thus obtained are mainly due to the strong internal correlations of quality traits between materials before and after processing, which could provide semi-quantitative prediction accuracy for practical uses. However, the internal correlations of quality traits during fruit processing still remain unreliable when using a large variability of raw materials and various industrial processing systems (Lan, Jaillais, Leca, Renard, & Bureau, 2020). Further, such a direct modelling method requires a necessary step of acquisition of the infrared information on raw material batches for each processing condition, in order to obtain the same matrix sizes of spectra and reference data for calibration.

Here, a potential strategy has been firstly proposed to build reconstructed MIR spectra of processed purees from the spectra of raw apple homogenates using a spectral transfer method. The high consistency of the specific fingerprints used in the PLS models obtained for both the real spectra and the reconstructed spectra, confirmed our choice for this modelling strategy. Compared to the direct modelling method, a great advantage of using spectral transfer strategy is that the calibration dataset only needs the infrared information and reference data of several processed purees and just a limited number of spectra of corresponding raw apples. For example, in our dataset of 2019, the reconstructed spectra of 27 different processed purees could be transferred from only 3 corresponding spectra of the same apple batches.

After a simple scanning of raw apple homogenates by MIRS, our models revealed the possibility to i) predict the quality of apple purees, such as viscosity, SSC and TA using a standard processing recipe (95 ℃ for 5 mins and grinding at 1500 rpm), even though a large variability of raw apples was used (different fruit thinning and cold storage periods); and ii) to monitor and anticipate the organoleptic properties of cooked purees under different processing strategies, which is relevant for the processors and market. For example, a higher viscosity and acidity in-mouth feeling (predicted η50 = 1.42 ± 0.09 Pa.s, predicted TA = 65.8 ± 3.5 meq/kg FW) were predicted with the recipe at 83 ℃ for 30 min and grinding speed of 1000 rpm than with the recipe at 95 ℃ for 30 min and grinding speed of 3000 rpm (predicted η 50 = 0.98 ± 0.14 Pa.s, predicted TA = 56.4 ± 4.5 meq/kg FW).

Conclusion

As far as we know, this is the first study that shows the ability of MIRS to estimate the quality of processed fruit products taking into account a large variability coming from agricultural practices, post-harvest storage and processing conditions along the whole processing chain. MIR technique provided reliable assessment of viscosity, averaged particle sizes and major compositions (SSC, DMC, TA and malic acid) of apple purees.

Further, a simple spectroscopic transfer method (direct standardization) was applied for the first time to develop the reconstructed spectra of purees from their corresponding spectra of raw apple homogenates. MIRS coupled with PLS regression obtained acceptable predictions of TA, DMC, SSC, viscosity (η 50 and η 100) and averaged particle sizes of the final puree based on their reconstructed spectra. With a simple scanning of raw apple homogenates, MIR technique opens the possibility to i) predict the quality of final purees under a standard processing procedure, which is beneficial for fruit processing sustainability; and even ii) to monitor the texture and tastes of purees under different processing conditions for a better management. -1788, 1718-1734, 1659-1678, 1636-1616, 1549-1558, 1157-1163 η 100 0.26-1.22 0.27 0.89 0.85 0.07 3.0 8 1780-1788, 1718-1734, 1659-1678, 1636-1616, 1549-1558, 1157-1163 AS-G' (Pa) Note: Puree spectra and references data from 'Golden Delicious' apples, including variability of two different thinning conditions, cold storage (during 0, 1, 3 and 6 months), three heating temperatures (70, 83 and 95 ℃) and three grinding levels (300, 1000, 3000 rpm). All results based on the SNV pre-treated MIR spectra at 900-1800 cm -1 . Rc 2 : determination coefficient of the calibration set; Rv 2 : determination coefficient of the validation set; RPD: the residual predictive deviation of validation set; the linkable regions based on the β-coefficients of PLS models with the RPD values higher than 2.5; "/" presented the unacceptable results with the RPD values lower than 2.5. Note: Puree spectra and references data from 'Golden Delicious' apples, including variability of two different thinning conditions, cold storage (during 0, 1, 3 and 6 months), three heating temperatures (70, 83 and 95 ℃) and three grinding levels (300, 1000, 3000 rpm). All results based on the SNV pre-treated MIR spectra at 900-1800cm -1 . Rc 2 : determination coefficient of the calibration set; Rv 2 : determination coefficient of the validation set; RPD: the residual predictive deviation of validation set; the linkable regions based on the β-coefficients of PLS models with the RPD values higher than 2.5; "/" presented the unacceptable results with the RPD values lower than 2.5. Data expressed in fresh weight (FW) or dry weight (DW); values correspond to the mean of 3 puree replications (3 kg per replication). Raw apples were stored at 4°C: from harvest (T0)

and during one (T1), three (T3) and six months (T6). Two conditions of fruit load during cultivation: non-thinning with 100% number of apples (Th-) and thinning with 50% number of apples (Th+) per tree. In grey, two way-ANOVA results obtained for Golden Delicious purees. ns, *, **, ***: Non significant or significant at P < 0.05, 0.01, 0.001 respectively. Data expressed in fresh weight (FW) or dry weight (DW); values correspond to the mean of 3 puree replications (3 kg per replication). Processing conditions variations were: three heating temperatures at 70°C, 83°C and 95°C for 30 min, and three grinding speeds at 300, 1000 and 3000 rpm at each temperature. In grey, Kruskal-Wallis results obtained on Golden Delicious purees. ns, *, **, ***: Non significant or significant at P < 0.05, 0.01, 0.001 respectively.

Figure 1 .

 1 Figure captions: Figure 1. Experimental scheme for apple production, puree preparation and the sample characterization by infrared spectroscopy and reference measurements.

Figure 2 .

 2 Figure 2. Overview of the applied methodology to exploit reconstructed MIR spectra of purees and multivariate regression.

Figure 3 .

 3 Figure 3. Principal Component Analysis on the SNV pre-treated MIR spectra (900-1800 cm -1 ) of purees cooked with thinned (Th+) and non-thinned (Th-) 'Golden Delicious' apples stored at 4°C during 0, 1, 3 and 6 months (T0, T1, T3 and T6): (a) the scores plot of the first two components (PC1 and PC2) related to fruit thinning; (b) the scores plot of the first two components (PC1 and PC2) related to storage periods; (c) the loading plot of PC1; (d) the loading plot of PC2.

Figure 4 .

 4 Figure 4. Maps of Factorial Discriminant Analysis (FDA) performed on the SNV pretreated MIR spectra (900-1800 cm -1 ) of purees cooked with: (a) three different temperatures (70 ℃, 83 ℃ and 95 ℃) and (b) three grinding speeds (G0 at 300 rpm, G1 at 1000 rpm and G3 at 3000 rpm); (c) the first factorial score ('F1') of heating temperature discrimination; (d) the first factorial score ('F1') of grinding discrimination; (e) the second factorial score ('F2') of grinding discrimination.

Figure S1 .

 S1 Figure S1. Overview of MIR spectra pre-processing, direct standardization (SD) and multivariate regression

Table 1

 1 Prediction of biochemical, structural and rheological properties of apple purees using PLS regression based on their MIR spectra between 900-1800 cm - 1 .

	Parameter	Range	SD	Rc 2	Rv 2	RMSEV RPD LVs	Linkable regions (cm -1 )
	η 50	0.57-2.28	0.39	0.91 0.87	0.19	3.2	7	1780

Table 2

 2 Prediction of biochemical, structural and rheological properties of apple purees using PLS regression based on their reconstructed MIR spectra of raw apple homogenates between 900-1800 cm -1 .

	Parameter	Range	SD Rc 2	Rv 2	RMSEV RPD LVs	Linkable regions (cm -1 )
	η 50	0.57-2.28 0.39 0.85	0.82	0.21	2.5	8	1720-1734, 1636-1614, 1556-1560, 1547-1533, 1506-1510, 1448-1470, 1157-1169
	η 100	0.26-1.22 0.27 0.86	0.83	0.10	2.6	9	1720-1734, 1661-1675, 1636-1616, 1549-1558, 1507-1512,1445-1468, 1157-1163
	d 4:3	196-920 197 0.89	0.84	76	2.6	9	1740-1745, 1701-1715, 1645-1659, 1583-1587, 1537--1541, 1508-1510, 1452-1470, 1100-1112
	DMC (g/g FW)	0.15-0.23 0.02 0.87	0.84	0.01	2.6	6	1161-1165, 1101-1107, 1084-1090, 1051-1063, 989-1001
	SSC (°Brix)	12.6-18.6 1.9 0.89	0.85	0.7	2.8	5	1101-1112, 1084-1090, 1051-1069, 997-1001
	TA (mmol H + /kg FW) 5.1-73.5 25.5 0.88	0.86	8.9	2.9	7	1715-1710, 1107-1113, 1082-1086, 1059-1063, 1038-1042, 1001-993
	pH	3.6-4.4	0.2 0.84	0.79	0.1	2.1	8	1715-1709, 1105-1110, 1016-1018, 1074-1072, 1038-1042
	malic (g/kg FW)	2.4-7.0	1.2 0.87	0.82	0.6	2.3	9	1713-1709, 1105-1109, 1080-1088, 1058-1064, 1016-1018, 1001-998
	fructose (g/kg FW)	34.9-98.7 15.0 0.82	0.72	8.9	1.7	11	/

Table S1

 S1 Biochemical, structural and rheological data of apple purees and ANOVA results.

	Fruit	Storage	η50	η100	G'	G''	Yield stress	d 4:3	d 3:2	DMC	SSC	TA	glucose	fructose	sucrose	malic acid	pH	AIS	AIS
	thinning	periods	Pa.s	Pa.s	Pa	Pa	Pa	-	-	g/g FW °Brix mmol H+/kg FW g/kg FW g/kg FW g/kg FW	g/kg FW		mg/g FW mg/g DW
		T0	1.28	0.77	3127.8 626.7	47.5	909.9 251.5	0.19	13.4	58.1	18.9	50.5	66.7	4.5	3.7	164.5	31.6
		T1	1.13	0.70	1960.2 466.7	21.9	694	351.9	0.19	15.0	54.4	15.4	49.4	59.1	2.8	3.8	147.2	27.6
	Th-
		T3	0.87	0.55	1849	453	13.9	339.8 205.9	0.20	14.1	46.7	18.6	84.1	84.8	3.6	4.0	140.0	27.3
		T6	0.92	0.50	1816	427	14	316.1 223.6	0.19	13.8	26.8	23.0	85.1	77.3	2.7	4.4	145.7	27.6
		T0	1.75	0.97	3375.1 816.4	52.1	831.6 231.6	0.21	15.5	70.9	23.5	85.3	64.4	5.5	3.6	163.3	33.9
		T1	1.54	0.94	2783.7 639.5	25.2	489	261.8	0.21	17.6	69.3	16.8	80.3	115.9	5.6	3.8	150.9	31.9
	Th+
		T3	1.25	0.70	2517.6	609	22.3	405.1 228.3	0.22	16.9	59.9	13.8	88.0	102.5	4.9	3.8	143.3	31.6
		T6	1.60	0.88	3168.2 751.7	33.9	393.5 255.1	0.23	17.5	34.7	23.8	95.7	44.0	3.6	4.3	150.3	34.8
	Storage time	significance	***	***	***	***	***	***	***	**	**	***	***	***	***	***	***	ns	*
		F-values	20.4	13.6	24.7	15.0	72.8	216.1	41.5	6.6	8.3	279.4	74.7	76.2	38.5	13.8	436.3	1.8	4.3
	Fruit thinning	significance	***	***	***	***	***	***	*	***	***	***	ns	***	**	***	***	**	**
		F-values	138.2	61.5	67.3	91.5	29.6	47.2	5.5	157.7	115.7	176.9	1.3	187.8	13.8	57.9	48.8	15.9	10.3

Table S2

 S2 Biochemical, textural and rheological data of apple purees and results of Kruskal-Wallis non-parametric test.

	Temperatures	Grinding speeds	η 50	η 100	G'	G''	Yield stress	d 4:3	d 3:2	DMC	SSC	TA	glucose	fructose	sucrose	malic acid pH	AIS	AIS
	℃	rpm	Pa.s	Pa.s	Pa	Pa	Pa	-	-	g/g FW °Brix mmol H+/kg FW g/kg FW g/kg FW g/kg FW	g/kg FW		mg/g FW mg/g DW
		300	1.27	0.89	9629.8 2389.5	97.7	583.8 103.6	0.17	13.8	63.6	16.1	67.9	70.9	6.3	3.9	26.8	158.0
	70	1000	1.42	0.91	3295.4 768.8	38.8	633.7 274.8	0.16	13.8	67.8	18.0	66.2	76.6	6.4	3.9	28.0	171.4
		3000	0.64	0.40	1111.3 215.6	10.2	353.5 207.6	0.17	14.4	69.1	17.3	65.5	86.0	6.6	3.9	28.1	165.1
		300	1.23	0.93	8437.5 2078.9	97.9	553.0 212.8	0.18	14.9	59.9	14.6	67.7	70.6	5.8	3.8	29.5	166.7
	83	1000	1.38	0.87	3036.9 764.4	35.6	647.8 324.8	0.16	13.4	69.8	17.4	71.9	75.2	5.2	3.9	25.6	159.6
		3000	0.91	0.54	1312.2 259.9	11.8	297.4 192.4	0.17	14.8	70.3	16.6	66.8	76.1	5.7	3.9	28.2	162.6
		300	1.93	1.16	3708.2 1101.3	30.0	492.9 262.1	0.17	14.9	60.2	17.0	72.6	66.9	4.8	3.9	27.9	160.8
	95	1000	1.44	0.84	1955.4 522.2	16.6	332.9 209.8	0.17	14.9	64.1	17.8	71.3	64.5	5.1	3.9	26.2	157.3
		3000	1.07	0.63	1399.4 362.2	14.0	206.4 153.2	0.17	14.8	62.0	18.9	69.2	65.8	5.4	3.8	26.2	154.1
	Temperature	significances	ns	ns	ns	ns	ns	*	ns	*	*	*	*	*	**	**	ns	ns	ns
		F-values	4.1	1.3	0.9	0.4	1.5	7.0	1.2	6.3	6.8	8.4	6.1	8.9	9.1	10.2	2.1	2.3	4.2
	Grinding speeds	significances	**	***	***	***	***	**	**	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns
		F-values	10.9	17.2	21.6	22.6	19.4	13.7	9.2	1.1	2.8	1.4	0.4	0.1	1.4	0.2	1.3	0.4	0.2

Acknowledgements

The authors thank Patrice Reling, Barbara Gouble, Marielle Boge, Caroline Garcia and Gisèle Riqueau (INRAE, SQPOV unit) for their technical help. The 'Interfaces' project is an Agropolis Fondation Flashship project publicly funded through the ANR (French Research Agency) under "Investissements d'Avenir" programme (ANR-10-LABX-01-001 Labex Agro, coordinated by Agropolis Fondation). Weijie Lan was supported by a doctoral grant from Chinese Scholarship Council.

Table S3 The results of sensitivity (in blue cells) and specificity (in yellow cells) from: