Effect of Popcorn (Zea mays var. everta) Popping Mode (Microwave, Hot Oil, and Hot Air) on Fumonisins and Deoxynivalenol Contamination Levels.

Mycotoxins are secondary metabolites that are produced by molds during their development. According to fungal physiological particularities, mycotoxins can contaminate crops before harvest or during storage. Among toxins that represent a real public health issue, those produced by Fusarium genus in cereals before harvest are of great importance since they are the most frequent in European productions. Among them, deoxynivalenol (DON) and fumonisins (FUM) frequently contaminate maize. In recent years, numerous studies have investigated whether food processing techniques can be exploited to reduce the levels of these two mycotoxins, which would allow the identification and quantification of parameters affecting mycotoxin stability. The particularity of the popcorn process is that it associates heat treatment with a particular physical phenomenon (i.e., expansion). Three methods exist to implement the popcorn transformation process: hot air, hot oil, and microwaves, all of which are tested in this study. The results show that all popping modes significantly reduce FUM contents in both Mushroom and Butterfly types of popcorn. The mean initial contamination of 1351 µg/kg was reduced by 91% on average after popping. For DON, the reduction was less important despite a lower initial contamination than for FUM (560 µg/kg). Only the hot oil popping for the Mushroom type significantly reduced the contamination up to 78% compared to unpopped controls. Hot oil popping appears to provide the most important reduction for the two considered mycotoxins for both types of popcorn (-98% and -58% average reduction for FUM and DON, respectively).

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

Macromolecules 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-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-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.

Prediction and detection of human epileptic seizures based on SIFT-MS chemometric data.

Although epilepsy is considered a public health issue, the burden imposed by the unpredictability of seizures is mainly borne by the patients. Predicting seizures based on electroencephalography has had mixed success, and the idiosyncratic character of epilepsy makes a single method of detection or prediction for all patients almost impossible. To address this problem, we demonstrate herein that epileptic seizures can not only be detected by global chemometric analysis of data from selected ion flow tube mass spectrometry but also that a simple mathematical model makes it possible to predict these seizures (by up to 4 h 37 min in advance with 92% and 75% of samples correctly classified in training and leave-one-out-cross-validation, respectively). These findings should stimulate the development of non-invasive applications (e.g., electronic nose) for different types of epilepsy and thereby decrease of the unpredictability of epileptic seizures.

Identification of lactic acid bacteria Enterococcus and Lactococcus by near-infrared spectroscopy and multivariate classification.

Lactic acid bacteria are important in numerous biological processes. The fabrication of cheese, for example, uses the lactic acid bacteria found in raw milk such as Lactococcus lactis as starters to improve the organoleptic properties of milk. Conventional methods to determine the genus and species of lactic acid bacteria isolated from raw milk involve genotyping and phenotyping, which require specific preparation and sample destruction. To improve on this situation, we present herein a simple and non-destructive screening method to discriminate between the Lactococcus and Enterococcus species most commonly found in raw milk (L. lactis, E. durans, E. faecalis, and E. faecium). The bacteria are grown on agar plates and assessed by using near-infrared spectroscopy in a spectral range from 800 to 2777 nm. Principle component analysis loading line plots highlight the inter-genus and inter-species differences at various wavelengths, which are mostly assigned to cell-wall compounds such as polysaccharides. The best artificial neural network identification models give 98.8% and 86.3% classification rates at the genus and species level, respectively, for an external validation set made of 80 samples. These results suggest that near-infrared spectroscopy may be used to identify lactic acid bacteria on agar medium.

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).

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 applications.

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%.