A Method for the Allotment of Maize Contaminated by Toxins

Deoxynivalenol and fumonisins pose a health concern and have economic consequences, so the European regulation CE 1126/2007 dictates the maximal content allowed in cereals. The direct measurement of mycotoxin content using the established method is not only time-consuming and tedious, but also destructive and cannot be used in a silo. Alternative tools such as infrared spectroscopy are therefore being studied. For the present investigation, spectral data collected from maize kernels contaminated naturally by mycotoxins were studied to predict the risk of deoxynivalenol and fumonisins. Discriminant models were used to create and identify batches that satisfy regulations for animal or human consumption

Stratégie de classement des lots de maïs en fonction de leurs teneurs en fusariotoxines par spectroscopie infrarouge

Dans le maïs, les teneurs maximales en déoxynivalénol (DON) et fumoni- sines (FUM) sont édictées par le règlement CE 1126/2007. La mesure directe de ces mycotoxines est un processus long et coûteux et n'est pas appropriée pour des mesures en flux. Une alternative a donc été développée avec l'ana- lyse infrarouge (IR). Le premier essai porte sur la discrimination des espèces de Fusarium par IR, sur milieu de culture. Nous avons démontré que cette discrimination est possible à 5 jours de culture pour F. graminearum, F. proliferatum, F. subglutinans et F. verticillioides. De plus, l'étude de la cinétique de crois- sance par IR nous a permis d'identifier 4 zones spectrales caractéristiques qui pourront éventuellement servir à un suivi en ligne. Lors du second essai, les modèles de classement des lots de maïs en fonction des teneurs en DON et FUM ont été préférés aux modèles de quanti- fication. En effet, ces derniers n'ont pas montré de performances suffisantes pour être utilisés en routine, bien que les approches SVM aient été très inté- ressantes. Les modèles de classement, qu'ils soient PLS, ANN ou SVM, ont permis d'isoler de grandes proportions de maïs sain du reste du lot. Enfin, un dernier essai a permis d'identifier deux limites au-delà des- quelles un échantillon de maïs a de fortes probabilités d'être contaminé en FUM : 200000 UFC/g de mycoflore totale et 3.5 ppm d'ergostérol. La combinaison de ces modèles devrait permettre un classement des lots de maïs pour le risque DON+FUM.The European regulation CE 1126/2007 dictates the maximal mycotoxins contents allowed in cereals. Their direct measurement with the reference methods is long and tedious. Furthermore, it is destructive and cannot be used at silo. Alternative tools such as infrared spectroscopy are studied. The objective of the first part of the study is to apply near infrared spectroscopy to identify and discriminate Fusarium isolates, grown on solid culture medium, without preparation of the sample. This approach should allow discrimination of Fusarium species most abundant in the corn : Fusarium graminearum, Fusarium proliferatum, Fusarium subglutinans, Fusarium verticillioides. The infrared spectra of 58 strains belonging to these four species were collected on a spectrometer. A model based on artificial neural networks was developed for the species discrimination. With this model, the correct classifiaction on the external validation set was very good (98.8%). The objective of the second part is to sort the corn samples regarding their deoxynivalenol and fumonisins contents. More than 2000 samples were used in this study. Their infrared spectra were collected on a near spectrometer, and they were referenced for their mycotoxins contents with chromatography methods. The performances of the infrared models developed to quantify the deoxynivalenol (DON) and the fumonisins (FUM) contents are not good enough to be used in the field, even if the support vector machines approach gives interesting results. Thus, qualitative models were developed to sort the samples in three classes : 'no risk for DON and FUM', 'risk for DON and/or FUM' and a middle class 'samples to be analysed by reference method'. The objective of the last part is to study the link between contents of ergosterol, fumonisins and fungal biomass (Colony Forming Units-CFU) in 117 corn sapmles. A fungal cell count was also done for 34 species. The near infared spectra of the corn samples were collected and used to predict the fungal biomass and the ergosterol contents

Updated Overview of Infrared Spectroscopy Methods for Detecting Mycotoxins on Cereals (Corn, Wheat, and Barley)

Each year, mycotoxins cause economic losses of several billion US dollars worldwide. Consequently, methods must be developed, for producers and cereal manufacturers, to detect these toxins and to comply with regulations. Chromatographic reference methods are time consuming and costly. Thus, alternative methods such as infrared spectroscopy are being increasingly developed to provide simple, rapid, and nondestructive methods to detect mycotoxins. This article reviews research conducted over the last eight years into the use of near-infrared and mid-infrared spectroscopy to monitor mycotoxins in corn, wheat, and barley. More specifically, we focus on the Fusarium species and on the main fusariotoxins of deoxynivalenol, zearalenone, and fumonisin B1 and B2. Quantification models are insufficiently precise to satisfy the legal requirements. Sorting models with cutoff levels are the most promising application

An infrared diagnostic system to detect causal agents of grapevine trunk diseases

In most vineyards worldwide, agents of grapevine trunk diseases represent a real threat for viticulture and are responsible for significant economic loss to the wine industry. The conventional microbiological isolation technique used to diagnose this disease is tedious and frequently leads to false negatives. Thus, a dire need exists for an alternative method to detect this disease. One possible way involves infrared spectroscopy, which is a rapid, nondestructive analytical tool that is commonly used for quality control of feed stuffs. In the present work, a midinfrared spectrometer was tested as a fast tool for detecting agents of grapevine trunk disease. Midinfrared spectra were collected from 70 Vitis vinifera L. cv. Cabernet-Sauvignon one year old trunk-wood samples that were infected naturally in one viticulture nursery of the south of France. The samples underwent polymerase chain reaction and morphological identification, and the results were correlated to the midinfrared spectra by using multivariate analysis to discriminate between noninfected and infected samples. Based on comparison with some control samples, the highest percentage of correct identification of fungal contamination when using the midinfrared spectroscopy method is 80%

Discrimination of lactic acid bacteria Enterococcus and Lactococcus by infrared spectroscopy and multivariate techniques

Raw milk is often described as a major source of lactic acid bacteria for indigenous lactic starter. These indigenous starters contribute to the sensorial quality of cheese. Raw milk, rich in Lactoccocus lactis may therefore be very interesting for the cheese making. Currently, the most commonly used methods to differentiate lactic acid bacteria, and particularly the closely related phenotypes Lactococcus and Enterococcus, are based on DNA sequencing, but the cost and time required for these analytical methods hinder their use for rapid screening of raw material. The present study therefore proposes a simple alternative method to identify and discriminate against Lactococcus and Enterococcus, at the genus, but also at the species level, that is based on collecting near infrared spectra directly from bacterial colonies in Petri dishes. The infrared spectra of 280 strains of Lactococcus and Enterococcus cultured on solid media were collected by using a spectrometer with a wavelength range of 908 to 1684 nm and a remote probe. The best Classification And Regression Trees models for genus and species discrimination gave an excellent classification rate of 87% on an external validation set (30 strains). Loading line plots, with prominent bands at 900–960 and 1270–1390 nm, confirmed that the source of variation was due to changes in the polysaccharides

Physicochemical characterization and study of molar mass of industrial gelatins by AsFlFFF-UV/MALS and chemometric approach

Industrial gelatins have different physicochemical properties that mainly depend of the raw materials origin and the extraction conditions. These properties are closely related to the molar mass distribution of these gelatins. Several methods exist to characterize molar mass distribution of polymer, including the Asymmetrical Flow Field Flow Fractionation method. The goal of this study is to analyze the relationship between physicochemical properties and the gelatins molar mass distribution obtained by Asymmetrical Flow Field Flow Fractionation. In this study, 49 gelatins samples extracted from pig skin are characterized in terms of gel strength and viscosity and their molar mass distribution are analyzed by Asymmetrical Flow Field Flow Fractionation coupled to an Ultraviolet and Multi Angle Light Scattering detector. This analytical method is an interesting tool for studying, simultaneously, the primary chains and the high-molar-mass fraction corresponding to the polymer chains. Correlation analysis between molar mass distribution data from the different fractions highlights the importance of high molar mass polymer chains to explain the gel strength and viscosity of gelatins. These results are confirmed by an additional chemometric approach based on the UV absorbance of gelatin fractograms to predict gel strength (r2Cal = 0.85) and viscosity (r2Cal = 0.79)

Assessing macro-element content in vine leaves and grape berries of vitis vinifera by using near-infrared spectroscopy and chemometrics

International audienceVine fertilization is a tool that allows winegrowers to influence and regulate the quality of their wine. Today, nutritional analysis is done by using a CHNS analyzer and mass spectroscopy. However, these methods are destructive and time consuming. Another approach is to use near-infrared (NIR) spectroscopy, which, when coupled with chemometric tools, allows users to develop prediction models. This approach is widely used today in agriculture. In this study, we focus on the relative amount of carbon (C), hydrogen (H), nitrogen (N), and sulfur (S), in dry matter (DM), and on the C:N ratio. The relative amount of these elements was obtained by applying NIR spectroscopy to 252 samples of various fresh and dried vine organs. Each partial least squares model was tested on an external prediction set. The coefficient of determination for prediction (r(2)), the root-mean-square error of prediction (RMSEP), and the ratio of performance of prediction (RPD) were obtained for C (0.49, 2.24% of DM, and 1.33 for fresh material with MSC; 0.45, 2.37% of DM, and 1.26 for dry material with MSC, respectively), H (0.56, 0.27% of DM, and 1.45 for fresh material with D1; 0.49, 0.30% of DM, and 1.32 for dry material with D1, respectively), N (0.91, 0.17% of DM, and 3.32 for fresh material with raw spectra; 0.95, 0.13% of DM, and 4.39 for dry material with MSC, respectively), S (0.47, 0.046% of DM, and 1.31 for fresh material with MSC; 0.46, 0.046% of DM, and 1.30 for dry material with D2, respectively), and the C:N ratio (0.85, 8.20, and 2.58 for raw spectra of fresh material; 0.87, 7.55, and 2.80 for dry material with D2, respectively). These results show that NIR spectroscopy can be used to assess the status of nitrogen nutrition in vines and to monitor the C:N ratio

Study of the relationship between red wine colloidal fraction and astringency by asymmetrical flow field-flow fractionation coupled with multi-detection

International audienceMacromolecules including condensed tannins and polysaccharides impact wine taste and especially astringency. Asymmetrical Flow-Field-Flow-Fractionation (AF4) coupled to UV detection (UV), multi-angle light scattering (MALS) and refractive index detection (dRI) has been proposed to separate red wine colloids. The present work aimed at relating AF4-mutidetection profiles with red wine astringency. Fifty commercial red wines characterized by a trained sensory panel were analysed by AF4-UV-MALS-dRI and UV & ndash;visible spectroscopy. The analytical data set was built by selecting the three variables most predictive of the astringency score from each table (UV, dRI, MALS, Mw distribution, and UV & ndash;visible spectra of whole wine, permeate and retentate A4F fractions) and analysed by principal component analysis. Red wine astringency was more related to variables extracted from the AF4 data than to UV- absorbance of the wine or permeate, confirming the relevance of AF4-multidetection for analysis of the colloidal fraction involved in this perception

Identification of lactic acid bacteria and rhizobacteria by ultraviolet-visible-near infrared spectroscopy and multivariate classification

International audienceThe biological processes of interest to agro-industry involve numerous bacterial species. Lactic acid bacteria produce metabolites capable of fermenting food products and modifying their organoleptic properties, and plant-growth-promoting rhizobacteria can act as biofertilizers, biostimulants, or biocontrol agents in agriculture. The protocol of conventional techniques for bacterial identification, currently based on genotyping and phenotyping, require specific sample preparation and destruction. The work presented herein details a method for rapid identification of lactic acid bacteria and rhizobacteria at the genus and species level. To develop the method, bacteria were inoculated on an agar medium and analyzed by near infrared (NIR) and ultraviolet-visible-NIR (UV-Vis-NIR) spectroscopy. Artificial neural network models applied to the UV-Vis-NIR spectra correctly identified the genus (species) of 70% (63%) of the lactic acid bacteria and 67% of the rhizobacteria on an independent prediction set of unknown bacterial strains. These results demonstrate the potential of UV-Vis-NIR spectroscopy to identify bacteria directly on agar plates