Predicting odor profile of food from its chemical composition: Towards an approach based on artificial intelligence and flavorists expertise.

Odor is central to food quality. Still, a major challenge is to understand how the odorants present in a given food contribute to its specific odor profile, and how to predict this olfactory outcome from the chemical composition. In this proof-of-concept study, we seek to develop an integrative model that combines expert knowledge, fuzzy logic, and machine learning to predict the quantitative odor description of complex mixtures of odorants. The model output is the intensity of relevant odor sensory attributes calculated on the basis of the content in odor-active comounds. The core of the model is the mathematically formalized knowledge of four senior flavorists, which provided a set of optimized rules describing the sensory-relevant combinations of odor qualities the experts have in mind to elaborate the target odor sensory attributes. The model first queries analytical and sensory databases in order to standardize, homogenize, and quantitatively code the odor descriptors of the odorants. Then the standardized odor descriptors are translated into a limited number of odor qualities used by the experts thanks to an ontology. A third step consists of aggregating all the information in terms of odor qualities across all the odorants found in a given product. The final step is a set of knowledge-based fuzzy membership functions representing the flavorist expertise and ensuring the prediction of the intensity of the target odor sensory descriptors on the basis of the products' aggregated odor qualities; several methods of optimization of the fuzzy membership functions have been tested. Finally, the model was applied to predict the odor profile of 16 red wines from two grape varieties for which the content in odorants was available. The results showed that the model can predict the perceptual outcome of food odor with a certain level of accuracy, and may also provide insights into combinations of odorants not mentioned by the experts.

Glycemic response, satiety, gastric secretions and emptying after bread consumption with water, tea or lemon juice: a randomized crossover intervention using MRI.

PURPOSE: Numerous studies, including our previous work with lemon juice, have reported that low-pH meals reduce the glycemic response to starchy foods. However, the underlying mechanism is not yet understood. Tea, for its polyphenol content, has also been investigated. The main objective of this research was to concurrently study gastric emptying, appetite perceptions and glycemic responses to bread consumed with water, tea, or lemon juice. METHODS: In this randomized, crossover intervention, ten participants consumed equal portions of bread (100 g) with 250 mL of water, water-diluted lemon juice, or black tea at breakfast. Gastric volumes, blood glucose concentrations and appetite perceptions were alternately assessed over 180 min using magnetic resonance imaging, the finger-prick method and visual analogue scales, respectively. RESULTS: Compared to water, lemon juice led to a 1.5 fold increase of the volume of gastric contents, 30 min after the meal (454.0 ± 18.6 vs. 298.4 ± 19.5 mL, [Formula: see text] ± SEM P < 0.00001). Gastric emptying was also 1.5 times faster (P < 0.01). Conversely, lemon juice elicited a lower glycemic response than water (blood glucose concentrations at t = 55 min were 35% lower, P = 0.039). Tea had no effect. Changes in appetite perceptions and gastric volumes correlated well, but with no significant differences between the meals. CONCLUSIONS: Lemon juice lowered the glycemic response and increased both gastric secretions and emptying rate. The results are compatible with the hypothesis that the reduction of the glycemic response is mainly due to the interruption of starch hydrolysis via the acid-inhibition of salivary α-amylase. TRIAL REGISTRATION NUMBER: NCT03265392, August 29, 2017.

Exploring the breakdown of dairy protein gels during in vitro gastric digestion using time-lapse synchrotron deep-UV fluorescence microscopy.

A novel time-lapse synchrotron deep-UV microscopy methodology was developed that made use of the natural tryptophan fluorescence of proteins. It enabled the monitoring in situ of the microstructural changes of protein gels during simulated gastric digestion. Two dairy gels with an identical composition, but differing by the coagulation mode, were submitted to static in vitro gastric digestion. The kinetics of gel particle breakdown were quantified by image analysis and physico-chemical analyses of digesta. The results confirm the tendency of rennet gels, but not acid gels, to form compact protein aggregates under acidic conditions of the stomach. Consequently, the kinetics of proteolysis were much slower for the rennet gel, confirming the hypothesis of a reduced pepsin accessibility to its substrate. The particle shapes remained unchanged and the disintegration kinetics followed an exponential trend, suggesting that erosion was the predominant mechanism of the enzymatic breakdown of dairy gels in these experimental conditions.

Tuning of patient-specific deformable models using an adaptive evolutionary optimization strategy.

We present and analyze the behavior of an evolutionary algorithm designed to estimate the parameters of a complex organ behavior model. The model is adaptable to account for patient's specificities. The aim is to finely tune the model to be accurately adapted to various real patient datasets. It can then be embedded, for example, in high fidelity simulations of the human physiology. We present here an application focused on respiration modeling. The algorithm is automatic and adaptive. A compound fitness function has been designed to take into account for various quantities that have to be minimized. The algorithm efficiency is experimentally analyzed on several real test cases: 1) three patient datasets have been acquired with the "breath hold" protocol, and 2) two datasets corresponds to 4-D CT scans. Its performance is compared with two traditional methods (downhill simplex and conjugate gradient descent): a random search and a basic real-valued genetic algorithm. The results show that our evolutionary scheme provides more significantly stable and accurate results.

Regularity based descriptor computed from local image oscillations.

This work presents a novel local image descriptor based on the concept of pointwise signal regularity. Local image regions are extracted using either an interest point or an interest region detector, and discriminative feature vectors are constructed by uniformly sampling the pointwise Hölderian regularity around each region center. Regularity estimation is performed using local image oscillations, the most straightforward method directly derived from the definition of the Hölder exponent. Furthermore, estimating the Hölder exponent in this manner has proven to be superior, in most cases, when compared to wavelet based estimation as was shown in previous work. Our detector shows invariance to illumination change, JPEG compression, image rotation and scale change. Results show that the proposed descriptor is stable with respect to variations in imaging conditions, and reliable performance metrics prove it to be comparable and in some instances better than SIFT, the state-of-the-art in local descriptors.

The infection algorithm: an artificial epidemic approach for dense stereo correspondence.

We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorithm. The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D information that allows the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to produce only the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, which propagate like an artificial epidemic over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.