Rasmus Netterstrøm

Nikolay Kutuzov
email: nkutuzov@sund.ku.dk

Sune Darkner
email: darkner@di.ku.dk

Maurits Jørring Pallesen
email: mauritsjpallesen@gmail.com

Martin Johannes Lauritzen
email: mlauritz@sund.ku.dk

Kenny Erleben

François Lauze
email: francois@di.ku.dk

Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images

Keywords: deep learning, single molecule localization, two-photon microscopy

Tracking single molecules is instrumental for quantifying the transport of molecules and nanoparticles in biological samples, e.g., in brain drug delivery studies. Existing intensity-based localisation methods are not developed for imaging with a scanning microscope, typically used for in vivo imaging. Low signal-to-noise ratios, movement of molecules out-of-focus, and high motion blur on images recorded with scanning two-photon microscopy (2PM) in vivo pose a challenge to the accurate localisation of molecules. Using data-driven models is challenging due to low data volumes, typical for in vivo experiments. We developed a 2PM image simulator to supplement scarce training data. The simulator mimics realistic motion blur, background fluorescence, and shot noise observed in vivo imaging. Training a Deep learning-assisted localisation of nanoparticles data-driven model with simulated data improves localisation quality in simulated images and shows why intensity-based methods fail.

Introduction

Tracking single molecules or nanoparticles helps to solve a variety of biomedical problems, e.g., drug design and delivery [START_REF] Kucharz | Post-capillary venules are the key locus for transcytosis-mediated brain delivery of therapeutic nanoparticles[END_REF], studying the regulation of gene expression [START_REF] Elf | Probing transcription factor dynamics at the single-molecule level in a living cell[END_REF], determining mechanisms of viral infections [START_REF] Liu | Effectively and efficiently dissecting the infection of influenza virus by quantum-dot-based singleparticle tracking[END_REF], and many others. Single-molecule (or particle) tracking (SMT) is becoming a popular tool to study mechanisms of transport at the nanoscale in living cells and tissues [START_REF] Godin | Single-nanotube tracking reveals the nanoscale organization of the extracellular space in the live brain[END_REF]. In vivo imaging, especially in scattering tissues like the brain, becomes an ultimate challenge for SMT due to (i) low photon budgets causing low signalto-noise ratio on recorded images, (ii) motion artefacts caused by the animal's breathing, heartbeat, movement, and (iii) severe motion blur caused by the slowness of scanning microscopy typically used in vivo. These factors challenge the accurate localisation and tracking of molecules on images recorded in vivo, for example with two-photon microscopy (2PM; Fig. 4a).

Object localisation and detection is a core topic in computer vision. Feature-based detection, using SIFT [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF], HoG [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF], to cite the most classically used, were replaced by deep learning (DL) approaches, notably [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Methods[END_REF][START_REF] Redmon | You only look once: Unified, real-time object detection[END_REF] and many were built for specialised situations in medical imaging. On images of single molecules recorded with CCD or EMCCD cameras, localisation is achievable even for images with low signal-to-noise ratio or(and) moderate motion blur [START_REF] Mortensen | Optimized localization analysis for single-molecule tracking and superresolution microscopy[END_REF][START_REF] Mortensen | Confined brownian motion tracked with motion blur: Estimating diffusion coefficient and size of confining space[END_REF]. The localisation algorithms, however, require writing down an expression for the distribution of fluorescence in recorded images, which may not be possible for imaging moving molecules with 2PM: random motion of molecules overlaid with the laser beam's motion (Fig. 4b) can make modelling the distribution of fluorescence on a recorded image prohibitively complex. Neural networks have the potential to approximate these complex distributions of fluorescence and return the desired position of the molecule. Deep learning (DL) has been predominantly used for the localisation of immobile molecules with wide-field microscopes equipped with a CCD or EMCCD camera [START_REF] Petrov | Measurement-based estimation of global pupil functions in 3D localization microscopy[END_REF][START_REF] Boyd | DeepLoco: Fast 3D Localization Microscopy Using Neural Networks[END_REF][START_REF] Nehme | Deep-STORM: super-resolution single-molecule microscopy by deep learning[END_REF] in various in vitro experiments but not yet for localisation of moving molecules with 2PM in living animals.

Experimental data recorded in vivo can be so scarce and expensive to collect that training of the neural networks is more likely to rely on artificial data. Simulating large training data sets, informed by well-known theories of microscope image formation, has been used to train deep networks to localise immobile objects on images recorded with a CCD or EMCCD camera [START_REF] Petrov | Measurement-based estimation of global pupil functions in 3D localization microscopy[END_REF][START_REF] Boyd | DeepLoco: Fast 3D Localization Microscopy Using Neural Networks[END_REF]. In vivo imaging in scattering tissues, e.g. in the brain, however, is typically done with scanning 2PM, which records images pixel-by-pixel, line-by-line (Fig. 4b), as opposed to collecting all image pixels simultaneously with a CCD camera. Here we show how to train a neural network to localise single simulated quantum dots (QDs) and how the commonly used intensity-based detections fails to localise the QD on pixel-by-pixel and line-by-line simulated images. We used a combination of realistic simulated data, based on modelling diffusion of QDs during exposure and realistic motion of the laser beam. Many DL approaches have been used for the detection and localisation of objects, both with natural images and medical imaging modalities too. We demonstrate how a neural network used for object localisation and detection for natural images, can accommodate the QD detection and localisation task by training it on simulated data, incorporating effects of motion blur caused by very different rates between diffusion motion and camera motion, and then allow localisation of QDs on images sampled from a simulated test dataset.

Results

We started by developing a protocol to generate realistic synthetic 2PM images for training the FRCNN model [START_REF] He | Mask R-CNN[END_REF][START_REF] Pytorch | Keypoint R-CNN[END_REF][START_REF] Paszke | Pytorch: An imperative style, high-performance deep learning library[END_REF], later used for the localization of QDs. By combining a simple Brownian-like motion of QDs with a deterministic motion of the laser beam we reproduced motion blur characteristics for scanning microscopy (Fig. 2). We experimentally measured point spread function (PSF) dimensions and estimated fluorescence background from experimental images (N bkg in Eq. 1) to mimic real images as close as possible. Finally, with Poisson noise, we simulated images that looked very similar to images recorded in the brain in vivo (Fig. 2).

In Figure 3, we highlight the primary issue of intensity-based detectors in four images. The intensity-based detector, TrackPy [START_REF] Allan | Soft-matter/trackpy: Trackpy V0[END_REF], detects high-intensity pixels even though the high-intensity pixels are not the trajectory centroid of the QD. Training directly on the centroid coordinate enables FCNN to detect the trajectory centroid of the QD with higher accuracy. Then we conducted three experiments to demonstrate further the value of including the trajectory centroid in the DL training pipeline. Each experiment had a different diffusion coefficient that provided a different motion blur. The first experiment had a diffusion coefficient of 0.75µm 2 /s, the second of 1µm 2 /s and the third of 1.75µm 2 /s. Besides the different diffusion coefficients, we tested the robustness of each model by changing the epsilon-threshold (ε) of the precision-recall curve; The epsilons were 1,2 and 3, corresponding to a distance of 70nm, 140nm and 210nm given a pixel size of 0.07µm.

Experiment 1 -0.75 µm 2 /s

This experiment concentrates on localising the trajectory centroid of a QD when point sources of light closely resemble the microscope's PSF. That ensures the trajectory centroid is close to the high-intensity pixels, hence less motion blur. We choose a realistic diffusion coefficient value of 0.75µm 2 /s. Table 1 This table shows the AUC-score of all three experiments. In the first column, the QD diffuse with 0.75µm 2 /s, in the second column, the QD diffuse with 1µm 2 /s and in the third column, the QD diffuse with 1.75µm

Experiment 2 -1 µm 2 /s

We increase the diffusion coefficient, thus allowing more motion blur. Figure 1 (c) illustrates how the trajectory steps get larger, and the high-intensity pixels diverge from the microscope PSF; the distance between the trajectory centroid and high-intensity pixels becomes unpredictable.

Discussion

To tackle the problem of accurate localization of QD in pixel-by-pixel recorded images, we developed a simulator that generates a particle undergoing a Brownian motion recorded by a two-photon laser scanning microscope. Implementing the motion of the laser beam during pixel-by-pixel image scanning allows the reproduction of complex motion blur typically observed in experimental data. Note, that our simulator simplifies local transport in vivo and 2PM. First, we compute the trajectory of the QD by Brownian motions and second, we only generate images with one QD showing. To simplify the simulation, we neglected the excess and readout noises [START_REF] Mortensen | Optimized localization analysis for single-molecule tracking and superresolution microscopy[END_REF] introduced by the detector (photomultiplier tube; PMT) and assumed that the actual number of recorded photons is Poisson-distributed. We show that realistic simulated data, which incorporates the effect of motion blur, non-specific background fluorescence, and Poisson shot noise, improves the deep learning-based localization of QDs, seen in experiments 1 to 3. The primary result of our experiments demonstrates that the FCNN is robust against motion blur and keeps an AUC score above 0.90, whereas TrackPy drops the AUC score to 0.47. But we only trained one model. So for future implementations, we intend to make a five-fold crossvalidation. To our knowledge, this is the first simulator of training data for 2PM. It opens for better and faster trajectory recovery and analysis, an invaluable tool to understand how molecules move in complex, turbid, biological tissues, e.g., in the brain.

Methods

Data description

We simulated a training dataset of 29838 images and three testing datasets of 2570, 2410 and 1870 images. We chose a training dataset size of 29838 to avoid overfitting, and we chose a test dataset size of 6850 because it is common practice to choose a test dataset that is 20% of the size of the training dataset.

The following parameters reproduce in vivo experiment parameters: All images were 24 pixels tall and 25 pixels wide, trajectories of the QDs were simulated with a set of diffusion coefficients between 0.75µm 2 /s and 1.75µm 2 /s, the pixel size was 0.07µm, the pixel exposure time was 2µs and the retracing time was 1.1ms. To ensure single-molecule localisation images contained 1 QD.

Deep Learning Model

We use the open-access, and easy-to-use PyTorch implementation of FRCNNkeypoint with a resNet backbone [START_REF] He | Deep residual learning for image recognition[END_REF] pre-trained on the MS COCO dataset [START_REF] Lin | Microsoft COCO: Common Objects in Context[END_REF].

Train Configuration

We only trained our model using 20 epochs because the model converged. Of the 20 models, we chose the model with the highest validation accuracy. Due to limited GPU memory, we chose a batch size of 6 images, a validation batch size of 4 images, and stochastic gradient descent. We tuned the learning rate to 0.001 [START_REF] Vision | Pytorch Vision[END_REF].

Materials

We used a commercial two-photon scanning microscope (FVMPE-RS) built by Olympus (Japan), equipped with a Ti:Sapphire laser and GaAsP detectors (photomultiplier tubes; PMTs). We used commercial QTracker655 quantum dots (QDs; ThermoFisher Scientific, USA), which we diluted 15 times in saline before intravenous injection into anaesthetized mice.

Animals and surgical procedures

All protocols involving mice were approved by the Danish National Committee on Health Research Ethics, in accord with the guidelines of the European Council's Convention for the Protection of Vertebrate Animals used for Experimental and Other Scientific Purposes, and complied with the ARRIVE guidelines. We used four male wild type mice (C57bl/6j; Janvier-labs). We followed standard surgical procedures including tracheotomy, cauterization, and craniotomy, described in detail elsewhere [START_REF] Kucharz | Post-capillary venules are the key locus for transcytosis-mediated brain delivery of therapeutic nanoparticles[END_REF]. QDs were injected intravenously and released in the the brain parenchyma by ablating a small capillary with high laser power.

4.6 Simulating 2PM images of diffusing QDs.

Laser beam motion simulation

First, we calculated the trajectory of the laser beam during image acquisition (Fig. 4b) assuming that the laser beam moves along straight-line segments at the constant speed of 2 µm per pixel (typically used in in vivo experiments) until it reaches the end of a scanning line. Then the laser beam is retraced (no photons are recorded during this phase) to the beginning of the next line, which takes 1.1 ms. The process is repeated until the whole image is recorded. We estimated the position of the laser beam with the same time step as for diffusion trajectories (δt).

Random walk

Second, we simulated a random walk trajectory (independently for all three coordinates, xyz) of QDs during image acquisition by cumulatively adding normally-distributed random numbers N (0, σ 2 ), with zero mean and variance σ 2 = 2Dδt, where D is the diffusion coefficient of the QDs and δ is a time step. To align the timestep of the simulator with the timestep δ, we needed to sample from two normal distributions. The first distribution uses a timestep of 2µm, whereas the second uses the retracing timestep of 1.1ms.

Point Spread Function

Third, we modelled the expected photon count in individual image pixels.

Assuming that the square of the laser light intensity (two photons are absorbed simultaneously at 2PM) near the focus is approximately a 3D Gaussian [START_REF] Zipfel | Nonlinear magic: multiphoton microscopy in the biosciences[END_REF] with standard deviations σ xy in the xy-plane and σ z (depth) along the optical axis (Fig. 4c), the expected number of photons is

N = N qd 2πσ 2 xy exp - (X -x) 2 + (Y -y) 2 2σ 2 xy • 1 2πσ 2 z exp - (Z -z) 2 2σ 2 z + N bkg (1)
with (X, Y, Z) the position of the QD, (x, y, z) the position of a laser beam, N qd is the total possible number of emitted photons by a QD in the focal plane in the direction of the objective, and N bkg is expected number of photons of the fluorescent background (autofluorescence). We used the value N bkg = 8.4, which was estimated by averaging the background fluorescence on images recorded in the brain. The value of N qd was adjusted to imitate the signal-tonoise ratio of the brain images. We used realistic, measured in vivo, parameters of the 2PM PSF: σ xy = 0.21 µm and σ z = 1.36 µm [START_REF] Kutuzov | Contributions of the glycocalyx, endothelium, and extravascular compartment to the blood-brain barrier[END_REF].

Localization of QDs

Several DL-based detectors have been proposed in recent years [START_REF] Redmon | You Only Look Once: Unified, Real-Time Object Detection[END_REF][START_REF] Lin | Focal Loss for Dense Object Detection[END_REF], summarised in a recent review [START_REF] Youzi | A review of object detection based on deep learning[END_REF]. Many of these detectors have been tailored to detect complex objects. QDs are simple practically point sources of light but they do not image as the microscope's PSF because of the motion blur. Furthermore, images of QDs can be very noisy due to the high fluorescence background or movement of the QD out of focus. For such problems, the Faster R-CNN-Keypoint (FRCNN) [START_REF] He | Mask R-CNN[END_REF][START_REF] Pytorch | Keypoint R-CNN[END_REF][START_REF] Paszke | Pytorch: An imperative style, high-performance deep learning library[END_REF], which predicts object bounding boxes along with key points, has proven very reliable and has available implementations and was adopted in this work.

Metrics

For training the model we have used classical detection metrics for the bounding boxes and keypoints predicted by FRCNN. Detection speed in frames per second is of no interest in our problem. Precision and recall [START_REF] Liu | Deep Learning for Generic Object Detection: A Survey[END_REF] are based on intersection over union (IoU):

IoU ( B, B) = area( B ∩ B) area( B ∪ B) (2)
with B is a predicted bounding box and B the associated ground truth box. Determining true positives (TP), false positives (FP), true negatives (TN), false negatives (FN) for both bounding box and keypoints are computed according to the COCO standard [START_REF] Lin | Microsoft COCO: Common Objects in Context[END_REF][START_REF]ppwwyyxx, rbg: pycocotools[END_REF]. For evaluation, we needed another metric since an intensity-based detector such as TrackPy does not predict bounding boxes [START_REF] Allan | Soft-matter/trackpy: Trackpy V0[END_REF]. Therefore, we computed the area under the curve (AUC) from the precision and the recall. In addition, we modified the precision and the recall seen in [START_REF] Youzi | A review of object detection based on deep learning[END_REF] to use an L2 distance instead of an IoU constraint.

precision = tp tp + f p (3)
recall = tp tp + f n (4)
That means we define a prediction to TP if the distance between the predicted key point and the ground truth key point is below or equal to a threshold ε. FP is defined as if the distance exceeds the threshold or the prediction is duplicated. At last, FN is defined as if the detector fails to predict. We then computed the precision and the recall using equation eqs. ( 3) and (4). Finally, we computed the area under the curve (AUC) using precision and recall. Precision tells the probability that a prediction is correct, whereas recall tells the probability of detecting all possible ground truths. The AUC combines the metric, e.g. If the AUC score is close to 1, the detector correctly predicts and detects all possible ground truths. If the AUC score is close to 0, the detector either poorly predicts, fails to predict or both.

Figure 1 (

 1 b) shows a QD moving with a diffusion coefficient of 0.75µm 2 /s,

Fig. 1

 1 Fig. 1 Four artificially simulated images (a-d) with diffusion coefficient going from 0 to 1.75µm 2 /s. In images b-d, the green circle is the starting point of the trajectory, and the cyan circle is the ending point. (a) show a QD that diffuses with a diffusion coefficient of 0µm 2 /s, showing the microscope PSF with noise. (b-d) show QD diffusing with increasing diffusion coefficient: 0.75, 1 and 1.75µm 2 /s.

Fig. 2 Fig. 3

 23 Fig. 2 Comparison of three artificially simulated images (a-c) with real experimental images of QDs in the brain (d-f ), recorded with 2PM. Note how the simulated data reproduces the motion blur: panels a, b, and f, show elongated fluorescence intensity distributions in the vertical direction due to the movement of the QDs. Scale bars are 500 nm wide.

Fig. 4

 4 Fig. 4 Imaging single quantum dots in the brain of living mice with two-photon microscopy (2PM). a A simplified scheme of the experimental setup: 2PM images quantum dots (QDs) inside the brain of a living, anaesthetised mouse by sweeping the laser beam across the brain and collecting fluorescence emitted by the QDs. b Images are recorded by moving the laser beam (by rotating x-and y-scan mirrors shown in a) along straight lines (solid red).Between the lines, the beam is retraced (dashed red): this ensures the same scanning speed as the laser beam. c Dimensions of the point spread function (PSF; drawn to scale) of a 2PM inside a brain[START_REF] Kutuzov | Contributions of the glycocalyx, endothelium, and extravascular compartment to the blood-brain barrier[END_REF] and a scheme of brain extracellular spaces -a 3D network of channels between brain cells membranes -where QDs are moving. In 2PM, the PSF represents the square of the laser intensity near the focus and it is approximately Gaussian, with standard deviation along the z-axis being ≈6 times larger than along x-and y-axes.

  2 /s. In all three columns, the FCNN model achieves the best AUC-score.

	D		0.75µm 2 /s			1µm 2 /s			1.75µm 2 /s
	nm	70	140	210	70	140	210	70	140	210
	TrackPy	0.06	0.42	0.73	0.04	0.30	0.61	0.02	0.19	0.47
	FCNN	0.48	0.91	0.99	0.41	0.87	0.98	0.26	0.72	0.92

allowing less motion blur. Table

1

, column 1 demonstrates that TrackPy and FCNN localise the trajectory centroid for localisation within 210nm with an AUC score of 0.73 and 0.99.

Table 1

 1

	, column 2 tells

2

/s, i.e., ten times larger than the diffusion coefficient used in experiment one. Figure

1

(d) illustrates how increasing motion blur scatters the high-intensity pixels across a larger area and, in some cases, makes the high-intensity pixels interchangeable for noise. Again we see in table 1, column three, that TrackPy drops in AUC performance, going from 0.61 to 0.47, whereas FCNN keeps an AUC score above 0.90 for localisation within 210nm.

Availability of data and materials

We released the data as open-access at the time of publication.

Code availability

Most of the code in this project is custom-made and will be open-access at the time of publication. However, we are happy to share the code upon request.

Declarations Funding

Not applicable

Conflict of interest/Competing interests

Not conflict or competing interests

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for publication

Not applicable

Authors' contributions

The first and second co-first authors contributed equally to this work. They made the primary work of this paper. The third, fifth and sixth authors contributed with writing, supervision and concept development. The fourth author helped with the programming


E Pislor

P Y Pontalier

J Albet
email: joel.albet@ensiacet.fr

PROCESS FOR RECOVERING CARBOXYLIC ACIDS FROM SUGAR CANE INDUSTRY BY-PRODUCTS

Keywords: recovery, organic acids, sugar cane industry by-products

Food industry by-products such as molasses and vinasses may provide an important source of organic acids. The aim of this study is to compare three processes, precipitation, chromatography and liquid-liquid extraction, for the recovery of carboxylic acids from sugarcane molasses from Réunion Island. Precipitation was performed with different temperatures by addition of calcium chloride. The results revealed that precipitation can recover aconitic acid efficiently from molasses. Liquid-liquid extraction was carried out with TBP in n-dodecane. The results indicated that the selectivity depends on solution pH. Extraction with molasses is hindered by the appearance of a third phase. Chromatography was performed on ion exchange resin (Amberlite IRA 900 Cl) from Rohm & Haas. The extraction yields were low in the synthetic solutions and the recovery of carboxylic acids from molasses was impossible due to their high viscosity.

Biorefineries play an important role in sustainable development by supplying the chemical and energy needs for society and by mitigating the environmental effects of fossil fuels and their rapid depletion. Sugar cane was originally cultivated to produce sugar and alcohol but is now also transformed to produce bioethanol. These productions generate a large quantity of co-products, such as molasses and vinasses, which may provide an important source of organic acids. Molasses are the non crystallized fraction of the sugar cane extract, while vinasses are the residues of the alcoholic distillation boiler.

The composition of those effluents, mainly nitrogen, carbon and potassium make them attractive as organic fertilizer. But as this practice may affect the soil properties new valorisation should be developed. According to their high carbon content, more specifically their organic components, molasses could be used as raw material to produce chemicals like organic acids. Aconitic and lactic acids appear to be the major organic carbon compound in sugar cane juice and industrial by-products [START_REF] Albet | HPLC determination of organic acids in sugar cane and its industrial by-products[END_REF]. Three processes have been identified for acid recovery: crystallisation, chromatography and solvent extraction [START_REF] Regna | Recovery of Aconitic Acid from Molasses[END_REF][START_REF] Joglekar | Comparative assessment of downstream processing options for lactic acid[END_REF].

Crystallisation can be managed either by concentration of the solution to crystallise carboxylic acids [START_REF] Inskeep | Lactic Acid from Corn Sugar[END_REF] or by alkaline salt addition, such as calcium chloride or magnesium chloride, for acid salt precipitation [START_REF] Ambler | Separation of aconitic acid from molasses[END_REF]. The latest method has been developed on a plant scale in Raceland (Louisiana) using Godchaux Sugars process to produce calcium magnesium aconitate [START_REF] Haines | Calcium Magnsium Aconitate[END_REF]. In order to obtain crude carboxylic acids, alkaline salts were dissolved with a strong acid, such as sulphuric acid, then the acid solution was filtered and the carboxylic acids were crystallised after evaporation [START_REF] Ventre | Process for the production of aconitic acid[END_REF]. Unfortunately, this process is not environmental friendly, because of the large quantity of waste produced during the dissolution -crystallisation step. Solvent-extraction processes using carbon-bonded oxygen donor extractants have been used to recover organic acids [START_REF] Azzam | Separation of aconitic acid from molasses by solvent extraction. Fette, Seifen[END_REF] but phosphorus-bonded oxygen-bearing solvents and tertiary amines, such as Tributylphosphate and Triisooctylamine [START_REF] Malmary | Measurement of partition coefficients of carboxylic acids between Water and Triisooctylamine dissolved in various diluents[END_REF], seem to be the best extractants. This technique gives good recovery yields but has never been developed on industrial scale. Finally, chromatography, using strong or weak base anion exchange resins, could be used to extract carboxylic acids from the fermentation broth [START_REF] Moldes | Resin selection and single-step production and recovery of lactic acid from pretreated wood[END_REF] or the fermented sugar cane juice [START_REF] Saska | Feasibility of recovering aconitic acid from low purity sugarcane liquors with ion exchange resins[END_REF]. Regeneration or acid recovery is done using different eluents, like salt solutions or strong acids or bases. The aim of this study is to compare the three main processes (precipitation, chromatography and liquidliquid extraction) for the recovery of carboxylic acids from sugarcane molasses with the present day technologies.

MATERIALS AND METHODS

Chemicals and raw material

Model solutions were prepared with trans-aconitic, citric, malic and lactic acids from Fluka and Aldrich and pH adjusted with sodium hydroxide from Acros. Tributylphosphate (TBP) and n-dodecane, respectively 97 % and 90 % of purity, were supplied by Fluka. Milli-Q water obtained by Milli-Q system (Millipore Corp.) was used to dilute acids.

Molasses from the sugar cane plant of Le Gol and vinasses from the distillery Rivière du Mât (Réunion Island) have been characterized. (

Analytical method

Organic acid contents have been evaluated using a high performance ionic chromatography system Dionex ICS-2000 equipped with a IonPac AS11 HC (4 x 250 mm) column, a IonPac AG11 HC (4 x 50 mm) guard column, a suppressor ASRS Ultra II 4 mm and conductimetric conductor DS6. 25 µL of each sample, previously diluted depending on its original concentration and filtered at 0,45 µm, was injected twice.

Precipitation

First of all, precipitation was tested on model solutions. Once the optimal temperature, pH, CaCl 2 quantity and aconitic acid concentration were determined using Doehlert experimental design, experiences on molasses were carried out.

Model solution experiments

Model solutions containing trans-aconitic acid from 1.5 to 4 % in mass were prepared and the pH was adjusted to 6.3 with a solution of sodium hydroxide at 12 % in mass. 250 g of the model solutions were introduced in a three necks reactor and heated under reflux about 15 min with stirring (300 rpm) from 50 to 90 °C.

Salt solutions of calcium chloride were prepared dissolving 0.7 to 14 g of CaCl 2 in 1 to 20 mL of distilled water according to the salt solubility.

The precipitation started when the salt solution was added to the solution of trans-aconitic acid, then temperature and stirring were maintained from 1 to 9 hours. At the end, stirring was stopped and the three necks reactor was left one hour at 25 °C in order to finalize the precipitation and decantate the solid. Finally, the solution was filtered under vacuum and the tricalcium aconitate was dried at 50 °C almost two days.

In order to evaluate the reaction yield (Equation 1), concentrations of initial trans-aconitic solution ([AAco] initial ) and filtrate ([AAco] filtrate ) were determined by HPIC.

Equation 1. Yp (%) = (([AAco] initial -[AAco] filtrate )/ [AAco] initial ) x 100

By-product solution experiments

Molasses were diluted to 50° Brix by adjusting the solution pH and adding the precipitation agent solution. The calcium hydroxide solution was added to the molasses to raise the pH from 6.2 to 6.8. Salt solutions of calcium chloride were prepared dissolving 7 to 11.5 g of CaCl 2 or 5 g of MgCl 2 in 120 mL distilled water according to the salt solubility. 250 g of treated molasses were introduced in the reactor and heated at 60 or 90 °C under reflux. Mechanic stirring (300 rpm) was applied during 30 min before adding the salt solution, which is the starting point of the precipitation reaction. After 1 or 7h, stirring and heating were stopped and the reactor was cooled at room temperature during 1h. The precipitated molasses were centrifuged 15 min at 5500 G. The tricalcium aconitate solid was washed with 150 mL of deionized water and centrifuged again. Finally, the solid was dried 12 hours at 106 °C.

1 g of the precipitate was dissolved in 30 mL of 25 % sulphuric acid, heated 30 min at 80°C under mechanic stirring, cooled 30 min in fresh water and filtered under vacuum to eliminate CaSO 4 solid wastes. Filtration cake was washed with deionized water in order to adjust the filtrate to 50 mL.

According to the concentration of acids in the filtrate and the initial solutions, solid purity (Equation 2) and precipitation yield (Equation 3) were calculated. [AAco] filtrate is the aconitic acid concentration in the filtrate; W filtrate is the mass of filtrate ; W precipitate is the mass of dissolved precipitate; W precipitate total is the whole mass of precipitate obtain after precipitation ;

[AAco] molasse is the aconitic acid concentration in the molasse and W molasse is the mass of molasse introduced initially.

Solvent extraction

Model solution experiments

An investigation of the partition coefficients of aconitic acid between water and solvent has been done. Experiments were carried out with model solutions of 0.5 % aconitic acid. The solvent mixture composed of solvent (TBP) and diluent (n-dodecane) were prepared. The volumic percentage ratio solvent:diluent was 30:70 or 60:40. Once the best ratio was defined, the separation of aconitic acid was studied with complex model solutions. Two types of solutions were prepared. In one hand, rich acid solutions were made of 1.6 % of aconitic acid; 1.2 % of citric acid and 0.5 % of malic acid and in another hand poor acid solutions were prepared with 0.3 % of aconitic acid, 0.05 % of citric acid and 0.09 % of malic acid (all percentages are in mass). Sodium hydroxide solution was introduced to the solutions in order to adjust their initial pH from 4.50 ± 0.04 to 5.52 ± 0.05. 25 mL of these aqueous solutions of acid were mixed with 15 or 30 mL of TBP and 35 or 20 mL of n-Dodecane and shaked during 3 hours. After settling for 1 hour 30, aqueous solution (raffinate) was collected and analysed by HPIC to evaluate the acid concentration after extraction. The partition coefficient (m), which is the ratio between the mass concentration of acid in the organic phase and in the aqueous phase and the extraction yield, which is the ratio between the mass of acids in the extracts and in the initial solutions, were calculated.

The organic phases which contain the carboxylic acids were regenerated by shaking 25 mL of extract with 25 mL of standard sodium hydroxide at 0.1 N during 3 hours and settling during 1h30. The aqueous phases were collected and analysed to calculate the regeneration rate, which is the ratio between the mass of acids in the aqueous phase and the mass of acids in the extract.

By-product solution experiments

25 mL of molasses were mixed with 30 mL of TBP and 20 mL of n-Dodecane and shaked during 3 hours. Unfortunately, settling is impossible because of the third phase created by emulsion between the molasses and the solvent. 15 min of centrifugation at 5500 G were necessary to break the third phase in order to collect the raffinate and the extract. The raffinate was analysed by HPIC and the extract was used for regeneration following the previous method.

Ion exchange resins

Amberlite IRA 900 chloride form (Rohm & Haas), which is an anionic strongly basic exchanger resin, was used for organic acid recovery from model solutions.

First of all, the solutions of trans-aconitic acid, concentrated at 0.5 % in mass, were prepared. The pH of these solutions was adjusted at 5.0 using a solution of sodium hydroxide at 12 % in mass. 50 mL of wet resin was packed on glass column. One liter of solution was applied on in the column at a flow rate of 0.14 mL/min. The fixation capacity of the resin was calculated using the inlet and outlet acids concentrations in the solutions.

Loaded resins were cleaned with 100 mL of Milli-Q water and the acids were eluted with 300 mL of a solution of 0.1 or 1 N sodium hydroxide. Measurements of pH variations were done during the whole experience using WTW pHmeter equipped with WTW SenTix 41 electrode.

RESULTS AND DISCUSSIONS Precipitation

The experiments on aconitic acid solutions showed that crystallisation steady state is reached after 7 h. In order to choose the best conditions for precipitating tricalcium aconitate from synthetic solutions, a Doehlert experimental design with three factors, aconitic acid concentration, temperature and initial pH of the solutions, was designed. A polynomial second degree equation has been established and statistically validated. The main conclusion is that the solutions should have a concentration of aconitic acid higher than 15 g/kg, the best yields were obtained for 30 g/kg.

Experiments on molasses were performed at different temperatures, with two reaction times and with different salts. The results (Table 2) indicated a high precipitation rate, about 50% after 1 h with the three kinds of salts evaluated. As the precipitation rates were almost the same, only one experiment was done during 7 h. Then the precipitation rate was slightly increased to 70 % using CaCl 2 and MgCl 2 salts.

Solvent extraction

The partition coefficients were calculated using results obtained on synthetic solutions of 5 g/kg of aconitic acid. The higher this coefficient is the better are the extractions of acids. The volumic percentage ratio TBP:n-dodecane of 60:40 gives the highest partition coefficients. For this reason, experiments on rich and poor acid solutions were done with this solvent mixture.

The rich acid solutions were similar to the molasses while the poor acid solutions were closer to the acid content in the vinasses. The extraction yields were high when pHs of the solutions were acid, close to 2. (Table 3) The extraction was efficient for the acids whatever the feed solution. However, the extraction yields of aconitic acid were the highest in both cases and the process yields reached from 80 % to 90 %.

The natural pHs of molasses and vinasses are respectively 5.5 and 4.5, much higher than 2. In order to define the influence of that parameter on the extraction yield, sodium hydroxide was added to the poor acid solutions to adjust pH to those two values. (Table3) When the pH was higher than the first acidity constant, the recovery yield of acids decreased or was null. During the extraction with the molasses a third layer appeared, affecting the settling and sampling. As centrifugation before extraction did not shunt the formation of this third layer, a post-extraction centrifugation was carried out in order to separate the two phases. High extraction yields were obtained only after acidification of the molasses (Table 4). At natural pH, no acids were extracted, while both the malic and aconitic acids were extracted after acidification of the solution at a pH about 2. The process yields obtained on molasses are closer than those of the rich acid solutions (Table 4). The regeneration step is efficient since it allows the removals 100 % of the extracted acids.

Feed

Ion exchange resins

Experiments were carried out with a strong base anion exchange resin in chloride form. Previous results [START_REF] Moldes | Resin selection and single-step production and recovery of lactic acid from pretreated wood[END_REF] indicated that the best fixation was obtained with a low anion exchange resin, but Saska and Zapata [START_REF] Saska | Feasibility of recovering aconitic acid from low purity sugarcane liquors with ion exchange resins[END_REF] The capacity of the IRA 900 Cl resin was 100 g of aconitic acid per kg of wet resin. When the resin is saturated, it cannot retain the acid anymore. (Table 5)

The low capacity of the resin was only adapted to the solution with a low concentration. The recovery of the acid from the resin is more efficient under acidic treatment but the alkali treatment is non selective. A poor acid solution was eluted from the column. Fixation yields were efficient for citric and aconitic acids whereas malic acid which reached about 50 %.

CONCLUSIONS

The liquid-liquid extraction is the most efficient method among the three tested for the recovery of carboxylic acids from model solutions or from by-products. However it seems difficult to use this method for extracting the organic acids from the molasses because of the appearance of a third phase. In the case of by-products extraction, the precipitation was the most efficient technology, while the resins were limited by their low capacity and the high viscosity of the effluent. Aconitic acid, the main organic acid compound, was preferably extracted than the other acids in the three used methods. Unfortunately, the purity of the extract is not high because of the co-extraction of components such as colorants or other acids. As a consequence, after extraction a purification step should be performed in order to obtain a pure acid solution.

NOTATIONS

AMal

Equation 2 :

 2 P (%) = ([AAco] filtrate x W filtrate ) / W precipitate ) x 100 Equation 3 : Yp (%) = ( (W precipitate total x P) /([AAco] molasse x W molasse ) ) x 100

Table 1

 1

			)
		Lactic	Malic	Citric	Cis-Aconitic	Trans-Aconitic
		(ppm)	(ppm)	(ppm)	(ppm)	(ppm)
	Vinasse DRM	3 880	1 068	353	2 101	5 896
	Molasse Le Gol	909	4 880	1 245	6 160	25 922

Table 1 .

 1 The average acid concentration in by-products during sugar campaign 2007

Table 2 .

 2 Recovery yields of aconitic acid (%) by precipitation from molasses using two reaction times and different chloride salts

	Reaction time (h)

Table 3 .

 3 Yields of extraction and regeneration of aconitic, malic and citric acids, in percent, from two types of synthetic solutions

	Feed			Extraction			Regeneration		Process
				Ys (%)			Ys' (%)		Ys'' (%)
	Solution	pH	AAco	ACit	AMal	AAco	ACit	AMal	AAco
	Rich acid 1.6	98.1±0.3	43.8±4.2	29.4±16.2	80.8±7.3	100	100	79.3±7.3
		2.2	97.1±0.7	47.3±2.1	44.1±2.3	100	100	100	95.7±6.4
	Poor acid	4.5	14.8±6.4	n	n	83.0±23.7	n	n	9.3±3.3
		5.5	5.8±1.7	n	n	n	n	n	n

Table 4 .

 4 Yields of extraction and regeneration of aconitic, malic and citric acid, in percent, from molasses from Le Gol

				Extraction			Regeneration		Process
				Ys (%)			Ys' (%)		Ys'' (%)
	Solution	pH	AAco	ACit	AMal	AAco	ACit	AMal	AAco
	Molasse	5.5 2.2	n 74.1±1.3	n 22.9±6.6	n n	/ 100	/ n	/ n	n 77.8±6.0

Table 5 .

 5 used a strong cationic resin because it requires acidification of the media. Percentage of carboxylic acids recovered by Amberlite IRA 900 chloride form using different feed solutions and two solutions of sodium hydroxide at 0.1 and 1 N

	Extraction	Regeneration
	Yc (%)	Yc' (%)