Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy

Evaluating Moisture Content in Immersion Vacuum-Cooled Sausages with Citrus Peel Extracts Using Hyperspectral Imaging

The moisture content of immersion vacuum-cooled sausages with modified casings containing citrus fruit extracts under different storage conditions was studied using hyperspectral imaging (HSI) associated with chemometrics. Different pre-processing combinations were applied to improve the robustness of the model. The partial least squares regression model, employing the full reflectance spectrum with pre-treatment of the standard normal variate, showed calibration coefficients of determination (Rc2) of 0.6160 and a root mean square error of calibration (RMSEC) of 2.8130%. For the first time, prediction maps developed via HSI visualized the distribution of moisture content in the immersion vacuum-cooled sausages with unique modified casings in response to fluctuating storage conditions. The prediction maps showed exact parts with high water content, which will help us to monitor and prevent mold growth. The combination of HSI with multivariate analysis not only quantifies changes in moisture content but also visually represents them in response to various casing treatments under different storage conditions, illustrating the significant potential for real-time inspection and early mold detection in sausages within the processed meat industry.

Low-Cost Pocket Fluorometer and Chemometric Tools for Green and Rapid Screening of Deoxynivalenol in Durum Wheat Bran

Cereal crops are frequently contaminated by deoxynivalenol (DON), a harmful type of mycotoxin produced by several Fusarium species fungi. The early detection of mycotoxin contamination is crucial for ensuring safety and quality of food and feed products, for preventing health risks and for avoiding economic losses because of product rejection or costly mycotoxin removal. A LED-based pocket-size fluorometer is presented that allows a rapid and low-cost screening of DON-contaminated durum wheat bran samples, without using chemicals or product handling. Forty-two samples with DON contamination in the 40-1650 µg/kg range were considered. A chemometric processing of spectroscopic data allowed distinguishing of samples based on their DON content using a cut-off level set at 400 µg/kg DON. Although much lower than the EU limit of 750 µg/kg for wheat bran, this cut-off limit was considered useful whether accepting the sample as safe or implying further inspection by means of more accurate but also more expensive standard analytical techniques. Chemometric data processing using Principal Component Analysis and Quadratic Discriminant Analysis demonstrated a classification rate of 79% in cross-validation. To the best of our knowledge, this is the first time that a pocket-size fluorometer was used for DON screening of wheat bran.

Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat

In this study, a method of mid-level data fusion with the fingerprint region was proposed, which was combined with the characteristic wavelengths that contain fingerprint information in NIR and FT-MIR spectra to detect the DON level in FHB wheat during wheat processing. NIR and FT-MIR raw spectroscopy data on normal wheat and FHB wheat were obtained in the experiment. MSC was used for pretreatment, and characteristic wavelengths were extracted by CARS, MGS and XLW. The variables that can effectively reflect fingerprint information were retained to build the mid-level data fusion matrix. LS-SVM and PLS-DA were applied to investigate the performance of the single spectroscopic model, mid-level data fusion model and mid-level data fusion with fingerprint information model, respectively. The experimental results show that mid-level data fusion with a fingerprint information strategy based on fused NIR and FT-MIR spectra represents an effective method for the classification of DON levels in FHB wheat samples.

Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network

Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382-1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels.

The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops

Infrared (IR) spectroscopy is increasingly being used to analyze food crops for quality and safety purposes in a rapid, nondestructive, and eco-friendly manner. The lack of sensitivity and the overlapping absorption characteristics of major sample matrix components, however, often prevent the direct determination of food contaminants at trace levels. By measuring fungal-induced matrix changes with near IR and mid IR spectroscopy as well as hyperspectral imaging, the indirect determination of mycotoxins in food crops has been realized. Recent studies underline that such IR spectroscopic platforms have great potential for the rapid analysis of mycotoxins along the food and feed supply chain. However, there are no published reports on the validation of IR methods according to official regulations, and those publications that demonstrate their applicability in a routine analytical set-up are scarce. Therefore, the purpose of this review is to discuss the current state-of-the-art and the potential of IR spectroscopic methods for the rapid determination of mycotoxins in food crops. The study critically reflects on the applicability and limitations of IR spectroscopy in routine analysis and provides guidance to non-spectroscopists from the food and feed sector considering implementation of IR spectroscopy for rapid mycotoxin screening. Finally, an outlook on trends, possible fields of applications, and different ways of implementation in the food and feed safety area are discussed.

Evaluation of pH in Sausages Stuffed in a Modified Casing with Orange Extracts by Hyperspectral Imaging Coupled with Response Surface Methodology

The pH values of sausages stuffed in natural hog casings with different modifications (soy lecithin, soy oil, orange extracts (OE) from waste orange peels, lactic acid in slush salt, and treatment time) after 16-day 4 °C storage were evaluated for the first time by hyperspectral imaging (350−1100 nm) coupled with response surface methodology (RSM). A partial least squares regression (PLSR) model was developed to relate the spectra to the pH of sausages. Spectral pretreatment, including first derivative, second derivative, multiplicative scatter correction (MSC), standard normal variate (SNV), normalization, and normalization, with different combinations was employed to improve model performance. RSM showed that only soy lecithin and OE interactively affected the pH of sausages (p < 0.05). The pH value decreased when the casing was treated with a higher concentration of soy lecithin with 0.26% OE. As the first and second derivatives are commonly used to eliminate the baseline shift, the PLSR model derived from absorbance pretreated by the first derivative in the full wavelengths showed a calibration coefficient of determination (R2) of 0.73 with a root mean square error of calibration of 0.4283. Twelve feature wavelengths were selected with a comparable R2 value compared with the full wavelengths. The prediction map enables the visualization of the pH evolution of sausages stuffed in the modified casings added with OE.

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.

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NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature.

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Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis.

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Online and handheld NIR spectrometers for wheat flour detection are the future trends.

Characterization of Polyphenol and Volatile Fractions of Californian-Style Black Olives and Innovative Application of E-nose for Acrylamide Determination

Californian-style black olives require a sterilization treatment that produces a carcinogenic contaminant, acrylamide. Thus, this compound was evaluated in two different olive cultivars using an electronic nose (E-nose). The sterilization intensity had a significant influence on the final phenol concentrations, acrylamide content, and volatile compounds. Increasing the sterilization intensity from 10 to 26 min (F0) reduced the phenol content, but it promoted acrylamide synthesis, leading to a wide range of this toxic substance. The Ester and phenol groups of volatile compounds decreased their content when the sterilization treatment increased; however, aldehyde and other volatile compound groups significantly increased their contents according to the thermal treatments. The compounds 4-ethenyl-pyridine, benzaldehyde, and 2,4-dimethyl-hexane are volatile compounds with unpleasant odours and demonstrated a high amount of influence on the differences found after the application of the thermal treatments. The “Manzanilla Cacereña” variety presented the highest amount of phenolic compounds and the lowest acrylamide content. Finally, it was found that acrylamide content is correlated with volatile compounds, which was determined using multiple linear regression analysis (R² = 0.9994). Furthermore, the aroma of table olives was analysed using an E-nose, and these results combined with Partial Least Square (PLS) were shown to be an accurate method (range to error ratio (RER) >10 and ratio of performance to deviation (RPD) >2.5) for the indirect quantification of this toxic substance.

Integrated Mycotoxin Management System in the Feed Supply Chain: Innovative Approaches

Exposure to mycotoxins is a worldwide concern as their occurrence is unavoidable and varies among geographical regions. Mycotoxins can affect the performance and quality of livestock production and act as carriers putting human health at risk. Feed can be contaminated by various fungal species, and mycotoxins co-occurrence, and modified and emerging mycotoxins are at the centre of modern mycotoxin research. Preventing mould and mycotoxin contamination is almost impossible; it is necessary for producers to implement a comprehensive mycotoxin management program to moderate these risks along the animal feed supply chain in an HACCP perspective. The objective of this paper is to suggest an innovative integrated system for handling mycotoxins in the feed chain, with an emphasis on novel strategies for mycotoxin control. Specific and selected technologies, such as nanotechnologies, and management protocols are reported as promising and sustainable options for implementing mycotoxins control, prevention, and management. Further research should be concentrated on methods to determine multi-contaminated samples, and emerging and modified mycotoxins.

Near-infrared spectroscopy as a tool for rapid screening of deoxynivalenol in wheat flour and its applicability in the industry

This study aimed to evaluate the applicability and efficiency of Near-Infrared Spectroscopy (NIR) by using dispersive NIR and Fourier Transform NIR to analyse 267 samples of Brazilian wheat flour contaminated with deoxynivalenol (DON). For this, Partial Least-squares Discriminant Analysis (PLS-DA) and Principal Component Analysis-Linear Discriminant Analysis (PC-LDA) were used as discriminatory methods. Next, the samples were classified according to the maximum tolerated limits (MTL) for DON in Brazil, 750 μg kg^-1, and two groups were established for the calibration set: category A (≤450 μg kg^-1), non-contaminated or below the MTL; and category B (>450 μg kg^-1), contaminated or above the MTL. Validation samples through PLS-DA showed correct classification rates in the range of 85-87.5% and presented a 10-15% error; for PC-LDA, the hit rate was over 85% with an error of 10-15%. The present findings demonstrate that NIR is an excellent alternative method to classify wheat flour samples according to DON content.

Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples

The present study aimed to evaluate the use of hyperspectral imaging (HSI)-NIR spectroscopy to assess the presence of DON and ergosterol in wheat samples through prediction and classification models. To achieve these objectives, a first set of bulk samples was scanned by HSI-NIR and divided into two subsamples, one that was analysed for ergosterol and another that was analysed for DON by HPLC. This method was repeated for a second larger set to build prediction and classification models. All the spectra were pretreated and statistically processed by PLS and LDA. The prediction models presented a RMSEP of 1.17 mg/kg and 501 µg/kg for ergosterol and DON, respectively. Classification achieved an encouraging accuracy of 85.4% for an independent validation set of samples. The results confirm that HSI-NIR may be a suitable technique for ergosterol quantification and DON classification of samples according to the EU legal limit for DON.

Hyperspectral Imaging Coupled with Multivariate Analysis and Image Processing for Detection and Visualisation of Colour in Cooked Sausages Stuffed in Different Modified Casings

A hyperspectral imaging system was for the first time exploited to estimate the core colour of sausages stuffed in natural hog casings or in two hog casings treated with solutions containing surfactants and lactic acid in slush salt. Yellowness of sausages stuffed in natural hog casings (control group, 20.26 ± 4.81) was significantly higher than that of sausages stuffed in casings modified by submersion for 90 min in a solution containing 1:30 (w/w) soy lecithin:distilled water, 2.5% wt. soy oil, and 21 mL lactic acid per kg NaCl (17.66 ± 2.89) (p < 0.05). When predicting the lightness and redness of the sausage core, a partial least squares regression model developed from spectra pre-treated with a second derivative showed calibration coefficients of determination (R_c²) of 0.73 and 0.76, respectively. Ten, ten, and seven wavelengths were selected as the important optimal wavelengths for lightness, redness, and yellowness, respectively. Those wavelengths provide meaningful information for developing a simple, cost-effective multispectral system to rapidly differentiate sausages based on their core colour. According to the canonical discriminant analysis, lightness possessed the highest discriminant power with which to differentiate sausages stuffed in different casings.

Detection of mycotoxins and toxigenic fungi in cereal grains using vibrational spectroscopic techniques: a review

Nutrition-rich cereal grains and oil seeds are the major sources of food and feed for human and livestock, respectively. Infected by fungi and contaminated with mycotoxins are serious problems worldwide for cereals and oil seeds before and after harvest. The growth and development activities of fungi consume seed nutrients and destroy seed structures, leading to dramatic declines of crop yield and quality. In addition, the toxic secondary metabolites produced by these fungi pose a well-known threat to both human and animals. The existence of fungi and mycotoxins has been a redoubtable problem worldwide for decades but tends to be a severe food safety issue in developing countries and regions, such as China and Africa. Detection of fungal infection at an early stage and of mycotoxin contaminants, even at a small amount, is of great significance to prevent harmful toxins from entering the food supply chains worldwide. This review focuses on the recent advancements in utilising infrared spectroscopy, Raman spectroscopy, and hyperspectral imaging to detect fungal infections and mycotoxin contaminants in cereals and oil seeds worldwide, with an emphasis on recent progress in China. Brief introduction of principles, and corresponding shortcomings, as well as latest advances of each technique, are also being presented herein.

Fourier transform near-infrared and mid-infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran

BACKGROUND

Deoxynivalenol (DON) is the most common Fusarium mycotoxin occurring in wheat and wheat-derived products, with several adverse and toxic effects in animals and humans. Although bran fractions produced by milling wheat have numerous health benefits, cereal bran is the part of the grain with the highest concentration of DON, thus representing a risk for consumers. Increased efforts have been made to develop analytical methods suitable for rapid DON screening.

RESULTS

The applicability of Fourier transform near-infrared (FTNIR), or mid-infrared (FTMIR) spectroscopy, and their combination for rapid analysis of DON in wheat bran, was investigated for the classification of samples into compliant and non-compliant groups regarding the EU legal limit of 750 µg kg^-1 . Partial least squares-discriminant analysis (PLS-DA) and principal component-linear discriminant analysis (PC-LDA) were employed as classification techniques using a cutoff value of 400 µg kg^-1 DON to distinguish the two classes. Depending on the classification model, overall discrimination rates were from 87% to 91% for FTNIR and from 86% to 87% for the FTMIR spectral range. The FTNIR spectroscopy gave the highest overall classification rate of wheat bran samples, with no false compliant samples and 18% false noncompliant samples when the PC-LDA classification model was applied. The combination of the two spectral ranges did not provide a substantial improvement in classification results in comparison with FTNIR.

CONCLUSIONS

Fourier transform near-infrared spectroscopy in combination with classification models was an efficient tool to screen many DON-contaminated wheat bran samples and assess their compliance with EU regulations. © 2018 Society of Chemical Industry.

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.

Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis

The purpose of this study is to use near-infrared reflectance (NIR) spectroscopy equipment to nondestructively and rapidly discriminate Fusarium-infected hulled barley. Both normal hulled barley and Fusarium-infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for Fusarium discrimination and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA) was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from the side of hulled barley without crease achieved 100% accuracy in discriminating the normal hulled barley and the Fusarium-infected hulled barley. These results demonstrated the feasibility of rapid discrimination of the Fusarium-infected hulled barley by combining multivariate analysis with the NIR spectroscopic technique, which is utilized as a nondestructive detection method.

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world's priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPD_cal value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPD _p and RER values.

Quality Assessment of Gentiana rigescens from Different Geographical Origins Using FT-IR Spectroscopy Combined with HPLC

Gentiana rigescens is a precious herbal medicine in China because of its liver-protective and choleretic effects. A method for the qualitative identification and quantitative evaluation of G. rigescens from Yunnan Province, China, has been developed employing Fourier transform infrared (FT-IR) spectroscopy and high performance liquid chromatography (HPLC) with the aid of chemometrics such as partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) regression. Our results indicated that PLS-DA model could efficiently discriminate G. rigescens from different geographical origins. It was found that the samples which could not be determined accurately were in the margin or outside of the 95% confidence ellipses. Moreover, the result implied that geographical origins variation of root samples were more obvious than that of stems and leaves. The quantitative analysis was based on gentiopicroside content which was the main active constituent in G. rigescens. For the prediction of gentiopicroside, the performances of model based on the parameters selected through grid search algorithm (GS) with seven-fold cross validation were better than those based on genetic algorithm (GA) and particle swarm optimization algorithm (PSO). For the SVM-GS model, the result was satisfactory. FT-IR spectroscopy coupled with PLS-DA and SVM-GS can be an alternative strategy for qualitative identification and quantitative evaluation of G. rigescens.

Occurrence, Toxicity, and Analysis of Major Mycotoxins in Food

Mycotoxins are toxic secondary metabolites produced by certain filamentous fungi (molds). These low molecular weight compounds (usually less than 1000 Daltons) are naturally occurring and practically unavoidable. They can enter our food chain either directly from plant-based food components contaminated with mycotoxins or by indirect contamination from the growth of toxigenic fungi on food. Mycotoxins can accumulate in maturing corn, cereals, soybeans, sorghum, peanuts, and other food and feed crops in the field and in grain during transportation. Consumption of mycotoxin-contaminated food or feed can cause acute or chronic toxicity in human and animals. In addition to concerns over adverse effects from direct consumption of mycotoxin-contaminated foods and feeds, there is also public health concern over the potential ingestion of animal-derived food products, such as meat, milk, or eggs, containing residues or metabolites of mycotoxins. Members of three fungal genera, Aspergillus, Fusarium, and Penicillium, are the major mycotoxin producers. While over 300 mycotoxins have been identified, six (aflatoxins, trichothecenes, zearalenone, fumonisins, ochratoxins, and patulin) are regularly found in food, posing unpredictable and ongoing food safety problems worldwide. This review summarizes the toxicity of the six mycotoxins, foods commonly contaminated by one or more of them, and the current methods for detection and analysis of these mycotoxins.

A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits

The rapid identification of mycotoxins such as deoxynivalenol and aflatoxin B₁ in agricultural commodities is an ongoing concern for food importers and processors. While sophisticated chromatography-based methods are well established for regulatory testing by food safety authorities, few techniques exist to provide a rapid assessment for traders. This study advances the development of a mid-infrared spectroscopic method, recording spectra with little sample preparation. Spectral data were classified using a bootstrap-aggregated (bagged) decision tree method, evaluating the protein and carbohydrate absorption regions of the spectrum. The method was able to classify 79% of 110 maize samples at the European Union regulatory limit for deoxynivalenol of 1750 µg kg^-1 and, for the first time, 77% of 92 peanut samples at 8 µg kg^-1 of aflatoxin B₁. A subset model revealed a dependency on variety and type of fungal infection. The employed CRC and SBL maize varieties could be pooled in the model with a reduction of classification accuracy from 90% to 79%. Samples infected with Fusarium verticillioides were removed, leaving samples infected with F. graminearum and F. culmorum in the dataset improving classification accuracy from 73% to 79%. A 500 µg kg^-1 classification threshold for deoxynivalenol in maize performed even better with 85% accuracy. This is assumed to be due to a larger number of samples around the threshold increasing representativity. Comparison with established principal component analysis classification, which consistently showed overlapping clusters, confirmed the superior performance of bagged decision tree classification.

Effective Identification of Low-Gliadin Wheat Lines by Near Infrared Spectroscopy (NIRS): Implications for the Development and Analysis of Foodstuffs Suitable for Celiac Patients

Scope

The aim of this work was to assess the ability of Near Infrared Spectroscopy (NIRS) to distinguish wheat lines with low gliadin content, obtained by RNA interference (RNAi), from non-transgenic wheat lines. The discriminant analysis was performed using both whole grain and flour. The transgenic sample set included 409 samples for whole grain sorting and 414 samples for flour experiments, while the non-transgenic set consisted of 126 and 156 samples for whole grain and flour, respectively.

Methods and Results

Samples were scanned using a Foss-NIR Systems 6500 System II instrument. Discrimination models were developed using the entire spectral range (400–2500 nm) and ranges of 400–780 nm, 800–1098 nm and 1100–2500 nm, followed by analysis of means of partial least square (PLS). Two external validations were made, using samples from the years 2013 and 2014 and a minimum of 99% of the flour samples and 96% of the whole grain samples were classified correctly.

Conclusions

The results demonstrate the ability of NIRS to successfully discriminate between wheat samples with low-gliadin content and wild types. These findings are important for the development and analysis of foodstuff for celiac disease (CD) patients to achieve better dietary composition and a reduction in disease incidence.

Rapid assessment of bioactive phenolics and methylxanthines in spent coffee grounds by FT-NIR spectroscopy

Spent coffee grounds (SCGs) are a great source of bioactive compounds with interest to pharmaceutical and cosmetic industries. Phenolics and methylxanthines are the main health related compounds present in SCG samples. Content estimation of these compounds in SCGs is of upmost importance in what concerns their profitable use by waste recovery industries. In the present work, near-infrared spectroscopy (NIRS) was proposed as a rapid and non-destructive technique to assess the content of three main phenolics (caffeic acid, (+)-catechin and chlorogenic acid) and three methylxanthines (caffeine, theobromine and theophylline) in SCG samples obtained from different coffee brands and diverse coffee machines. The content of these compounds was determined for 61 SCG samples by HPLC coupled with diode-array detection. Partial least squares (PLS) regression based models were calibrated to correlate diffuse reflectance NIR spectra against the reference data for the six parameters obtained by HPLC. Spectral wavelength selection and number of latent variables were optimized by minimizing the cross-validation error. PLS models showed good linearity with a coefficient of determination for the prediction set (Rp(2)) of 0.95, 0.92, 0.88, 071 and 0.84 for caffeine, caffeic acid, (+)-catechin, chlorogenic acid and theophylline, respectively. The range error ratio (RER) was higher for caffeine (17.8) when compared to other compounds (12.0, 10.1, 7.6 and 9.2, respectively for caffeic acid, (+)-catechin, chlorogenic acid and theophylline). Moreover, the content of caffeine could be used to predict the antioxidant properties of SCG samples (R=0.808, n=61), despite not presenting this property itself. The results obtained confirmed that NIRS is a suitable technique to screen SCG samples unveiling those with high content of bioactive compounds, which are interesting for subsequent extraction procedures.

Advancements in IR spectroscopic approaches for the determination of fungal derived contaminations in food crops

Infrared spectroscopy is a rapid, nondestructive analytical technique that can be applied to the authentication and characterization of food samples in high throughput. In particular, near infrared spectroscopy is commonly utilized in the food quality control industry to monitor the physical attributes of numerous cereal grains for protein, carbohydrate, and lipid content. IR-based methods require little sample preparation, labor, or technical competence if multivariate data mining techniques are implemented; however, they do require extensive calibration. Economically important crops are infected by fungi that can severely reduce crop yields and quality and, in addition, produce mycotoxins. Owing to the health risks associated with mycotoxins in the food chain, regulatory limits have been set by both national and international institutions for specific mycotoxins and mycotoxin classes. This article discusses the progress and potential of IR-based methods as an alternative to existing chemical methods for the determination of fungal contamination in crops, as well as emerging spectroscopic methods.

Rapid analysis of deoxynivalenol in durum wheat by FT-NIR spectroscopy

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50–16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%–90% and 3%–7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation.

Recent advances in the rapid acoustic screening of deoxynivalenol in wheat grains

Deoxynivalenol (DON) is a natural and ubiquitous toxic metabolite produced by filamentous fungi of the genus Fusarium. Approximately one quarter of the world's food crops (mainly cereals) are affected by mycotoxins such as DON. A rapid and non-destructive method to evaluate the quality and safety of grains is therefore required to eliminate these toxins from the food chain. The first portable acoustic device that predicts the concentration of DON in cereal grains has been developed using a broadband capacitive ultrasonic transducer. An acoustic method was optimised for the rapid prediction of DON in wheat. To measure the performance of this method, a model system comprising 0-100% scabby wheat grains was prepared and a single laboratory validation was carried out. The best regression model between DON concentrations determined by the reference ELISA method and the acoustic technique was obtained at an acoustic frequency of 32.2 kHz, with a correlation coefficient of 0.9852 and a repeatability coefficient of variation of 2.1-9.3%, which is much better than the results achieved by prototype acoustic spectrometers. These data show that acoustic technology allows the online monitoring of DON in cereal grains, such as wheat, because it is possible to analyse multilayer grain beds. Sound absorption depends on the grain size and moisture content, so it is advisable to use the equipment at the point of harvest, where one strain of cereals usually dominates and the grains have a more homogeneous morphology and uniform moisture content.

State of the art in feedstuff analysis: a technique-oriented perspective

The need for global feed supply traceability, the high-throughput testing demands of feed industry, and regulatory enforcement drive the need for feed analysis and make extremely complex the issue of the control and evaluation of feed quality, safety, and functional properties, all of which contribute to the very high number of analyses that must be performed. Feed analysis, with respect to animal nutritional requirements, health, reproduction, and production, should be multianalytically approached. In addition to standard methods of chemical analysis, new methods for evaluation of feed composition and functional properties, authenticity, and safety have been developed. Requirements for new analytical methods emphasize performance, sensitivity, reliability, speed, simplified use, low cost for high volume, and routine assays. This review provides an overview of the most used and promising methods for feed analysis. The review is intentionally focused on the following techniques: classical chemical analysis; in situ and in vitro methods; analytical techniques coupled with chemometric tools (NIR and sensors); and cell-based bioassays. This review describes both the potential and limitations of each technique and discusses the challenges that need to be overcome to obtain validated and standardized methods of analysis for a complete and global feed evaluation and characterization.

Signal amplification using colloidal gold in a biolayer interferometry-based immunosensor for the mycotoxin deoxynivalenol

Deoxynivalenol (DON) is a toxin produced by certain species of Fusarium fungi that can infest wheat, barley and corn. The fungi cause diseases in crops worldwide and some of the secondary metabolites, such as DON, can adversely affect animal health and food safety. To monitor DON in wheat rapidly, a biosensor using the principle of biolayer interferometry (BLI) was developed. The signal from the sensor was substantially amplified through the use of a primary antibody-colloidal gold conjugate. The amplification was much greater in the presence of wheat matrix than in buffered solution, suggesting matrix components may have contributed to the enhancement. The improved signal provided by the amplification allowed for the development of rapid qualitative and quantitative assays. The limit of detection of the method was 0.09 mg kg(-1); the limit of quantitation was 0.35 mg kg(-1). Recovery from wheat spiked over the range from 0.2 to 5 mg kg(-1) averaged 103% (RSD = 12%). The quantitative assay compared favourably (r(2) = 0.9698) with a reference chromatographic method for 40 naturally contaminated wheats. The qualitative assay was able to classify accurately the same group of 40 samples as either above or below a 0.5 mg kg(-1) threshold. These results suggest that the BLI technique can be used to measure DON in wheat rapidly.