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

Recent progress and perspectives of metal organic frameworks (MOFs) for the detection of food contaminants

Over the past decades, increasing research in metal-organic frameworks (MOFs) being a large family of highly tunable porous materials with intrinsic physical properties, show propitious results for a wide range of applications in adsorption, separation, electrocatalysis, and electrochemical sensors. MOFs have received substantial attention in electrochemical sensors owing to their large surface area, active metal sites, high chemical and thermal stability, and tunable structure with adjustable pore diameters. Benefiting from the superior properties, MOFs and MOF-derived carbon materials act as promising electrode material for the detection of food contaminants. Although several reviews have been reported based on MOF and its nanocomposites for the detection of food contaminants using various analytical methods such as spectrometric, chromatographic, and capillary electrophoresis. But there no significant review has been devoted to MOF/and its derived carbon-based electrodes using electrochemical detection of food contaminants. Here we review and classify MOF-based electrodes over the period between 2017 and 2022, concerning synthetic procedures, electrode fabrication process, and the possible mechanism for detection of the food contaminants which include: heavy metals, antibiotics, mycotoxins, and pesticide residues. The merits and demerits of MOF as electrode material and the need for the fabrication of MOF and its composites/derivatives for the determination of food contaminants are discussed in detail. At last, the current opportunities, key challenges, and prospects in MOF for the development of smart sensing devices for future research in this field are envisioned.

Recent Advances in Mycotoxin Determination in Fish Feed Ingredients

Low-cost plant-based sources used in aquaculture diets are prone to the occurrence of animal feed contaminants, which may in certain conditions affect the quality and safety of aquafeeds. Mycotoxins, a toxic group of small organic molecules produced by fungi, comprise a frequently occurring plant-based feed contaminant in aquafeeds. Mycotoxin contamination can potentially cause significant mortality, reduced productivity, and higher disease susceptibility; thus, its timely detection is crucial to the aquaculture industry. The present review summarizes the methodological advances, developed mainly during the past decade, related to mycotoxin detection in aquafeed ingredients, namely analytical, chromatographic, and immunological methodologies, as well as the use of biosensors and spectroscopic methods which are becoming more prevalent. Rapid and accurate mycotoxin detection is and will continue to be crucial to the food industry, animal production, and the environment, resulting in further improvements and developments in mycotoxin detection techniques.

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.

Infrared Spectroscopy–Quo Vadis?

Given the exquisite capability of direct, non-destructive label-free sensing of molecular transitions, IR spectroscopy has become a ubiquitous and versatile analytical tool. IR application scenarios range from industrial manufacturing processes, surveillance tasks and environmental monitoring to elaborate evaluation of (bio)medical samples. Given recent developments in associated fields, IR spectroscopic devices increasingly evolve into reliable and robust tools for quality control purposes, for rapid analysis within at-line, in-line or on-line processes, and even for bed-side monitoring of patient health indicators. With the opportunity to guide light at or within dedicated optical structures, remote sensing as well as high-throughput sensing scenarios are being addressed by appropriate IR methodologies. In the present focused article, selected perspectives on future directions for IR spectroscopic tools and their applications are discussed. These visions are accompanied by a short introduction to the historic development, current trends, and emerging technological opportunities guiding the future path IR spectroscopy may take. Highlighted state-of-the art implementations along with novel concepts enhancing the performance of IR sensors are presented together with cutting-edge developments in related fields that drive IR spectroscopy forward in its role as a versatile analytical technology with a bright past and an even brighter future.

An effective detection method for wheat mold based on ultra weak luminescence

It is widely known that mold is one of important indices in assessing the quality of stored wheat. First, mold will decrease the quality of wheat kernels; the wheat kernels infected by mold can produce secondary metabolites, such as aflatoxins, ochratoxin A, zearalenone, fumonisins and so on. Second, the mycotoxins metabolized by mycetes are extremely harmful to humans; once the food or feed is made of by those wheat kernels infected by mold, it will cause serious health problems on human beings as well as animals. Therefore, the effective and accurate detection of wheat mold is vitally important to evaluate the storage and subsequent processing quality of wheat kernels. However, traditional methods for detecting wheat mold mainly rely on biochemical methods, which always involve complex and long pretreatment processes, and waste part of wheat samples for each detection. In view of this, this paper proposes a type of eco-friendly and nondestructive wheat mold detection method based on ultra weak luminescence. The specific implementation process is as follows: firstly, ultra weak luminescence signals of the healthy and the moldy wheat subsamples are measured by a photon analyzer; secondly, the approximate entropy and multiscale approximate entropy are introduced as the main classification features separately; finally, the detection model has been established based on the support vector machine in order to classify two types of wheat subsamples. The receiver operating characteristic curve of the newly established detection model shows that the highest classification accuracy rate can reach 93.1%, which illustrates that our proposed detection model is feasible and promising for detecting wheat mold.

A review combining emerging techniques with classical ones for the determination of biscuit quality: advantages and drawbacks

The production of biscuit and biscuit-like products has faced many challenges due to changes in consumer behavior and eating habits. Today's consumer is looking for safe products not only with fresh-like and pleasant taste, but also with long shelf life and health benefits. Therefore, the potentiality of the use of healthier fat and the incorporation of natural antioxidant in the formulation of biscuit has interested, recently, the attention of researchers. The determination of the biscuit quality could be performed by several techniques (e.g., physical, chemical, sensory, calorimetry and chromatography). These classical analyses are unfortunately destructive, expensive, polluting and above all very heavy, to implement when many samples must be prepared to be analyzed. Therefore, there is a need to find fast analytical techniques for the determination of the quality of cereal products like biscuits. Emerging techniques such as near infrared (NIR), mid infrared (MIR) and front face fluorescence spectroscopy (FFFS), coupled with chemometric tools have many potential advantages and are introduced, recently, as promising techniques for the assessment of the biscuit quality.

A review on the application of spectroscopy to the condiments detection: from safety to authenticity

Condiments are the magical ingredients that make the food present a richer taste. In recent years, due to the increasing consciousness of food safety and human health, much progress has been made in developing rapid and nondestructive techniques for the evaluation of food condiments safety, authentication, and traceability. The potential of spectroscopy techniques, such as near-infrared (NIR), mid-infrared (MIR), Raman, fluorescence, inductively coupled plasma (ICP), and hyperspectral imaging techniques, has been widely enhanced by numerous applications in this field because of their advantages over other analytical techniques. Following a brief introduction of condiment and safety basics, this review mainly focuses on recent vibrational and atomic spectral applications for condiment nondestructive analysis and evaluation, including (1) chemical hazards detection; (2) microbiological hazards detection; and (3) authenticity concerns. The review shows current spectroscopies to be effective tools that will play indispensable roles for food condiment evaluation. In addition, online/real-time applications of these techniques promise to be a huge growth field in the near future.

Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)

Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared Spectroscopy (NIR) has made great headway for being an easy-to-use technology. NIR was applied in the present research to quantify the contamination level of total FBs, i.e., fumonisin B1+fumonisin B2 (FB1+FB2), and ZEN in Brazilian maize. From a total of six hundred and seventy-six samples, 236 were analyzed for FBs and 440 for ZEN. Three regression models were defined: one with 18 principal components (PCs) for FB1, one with 10 PCs for FB2, and one with 7 PCs for ZEN. Partial least square regression algorithm with full cross-validation was applied as internal validation. External validation was performed with 200 unknown samples (100 for FBs and 100 for ZEN). Correlation coefficient (R), determination coefficient (R2), root mean square error of prediction (RMSEP), standard error of prediction (SEP) and residual prediction deviation (RPD) for FBs and ZEN were, respectively: 0.809 and 0.991; 0.899 and 0.984; 659 and 69.4; 682 and 69.8; and 3.33 and 2.71. No significant difference was observed between predicted values using NIR and reference values obtained by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS), thus indicating the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs and ZEN contamination. The external validation confirmed a fair potential of the model in predicting FB1+FB2 and ZEN concentration. This is the first study providing scientific knowledge on the determination of FBs and ZEN in Brazilian maize samples using NIR, which is confirmed as a reliable alternative methodology for the analysis of such toxins.

Infrared attenuated total reflection spectroscopic surface analysis of bovine-tail intervertebral discs after UV-light-activated riboflavin-induced collagen cross-linking

The tensile strength of the intervertebral disc (IVD) is mainly maintained by collagen cross-links. Loss of collagen cross-linking combined with other age-related degenerative processes contributes to tissue weakening, biomechanical failure, disc herniation and pain. Exogenous collagen cross-linking has been identified as an effective therapeutic approach for restoring IVD tensile strength. The current state-of-the-art method to assess the extent of collagen cross-linking in tissues requires destructive procedures and high-performance liquid chromatography. In this study, we investigated the utility of infrared attenuated total reflection (IR-ATR) spectroscopy as a nondestructive analytical strategy to rapidly evaluate the extent of UV-light-activated riboflavin (B2)-induced collagen cross-linking in bovine IVD samples. Thirty-five fresh bovine-tail IVD samples were equally divided into five treatment groups: (a) untreated, (b) cell culture medium Dulbecco's Modified Eagle's Medium only, (c) B2 only, (d) UV-light only and (e) UV-light-B2. A total of 674 measurements have been acquired, and were analyzed via partial least squares discriminant analysis. This classification scheme unambiguously identified individual classes with a sensitivity >91% and specificity >92%. The obtained results demonstrate that IR-ATR spectroscopy reliably differentiates between different treatment categories, and promises an excellent tool for potential in vivo, nondestructive and real-time assessment of exogenous IVD cross-linking.

Application of advanced molecular spectroscopy and modern evaluation techniques in canola molecular structure and nutrition property research

This review aims to provide research update and progress on applications of advanced molecular spectroscopy to current research on canola related bio-processing technology, molecular structure, and nutrient utilization and availability. The studies focused on how inherent molecular structure changes affect nutritional quality of canola and its co-products from bio-processing. The molecular spectroscopic techniques (SR-IMS, DRIFT, ATR-FTIR) used for molecular structure and nutrition association were reviewed, including the synchrotron radiation with infrared microspectroscopy, the synchrotron radiation with soft x-ray microspectroscopy, the diffuse reflectance infrared Fourier transform spectroscopy, the grading near infrared reflectance spectroscopy, and the Fourier transform infrared vibrational spectroscopy. Nutritional evaluation with other techniques in association with molecular structure was also reviewed. This study provides updated research progress on application of molecular spectroscopy in combination with various nutrition evaluation techniques to current research in the canola-related bio-oil/bio-energy processing and nutrition sciences.

Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectra and olfactory sensors

BACKGROUND

Fungal contamination in food products leads to mustiness, biochemical changes, and undesirable odors, which result in lower food quality and lower market value. To develop a rapid method for detecting fungi, hyperspectral imaging (HSI) was applied to identify five fungi inoculated on plates (Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum, Aspergillus fumigatus, and Aspergillus ochraceus). Near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, and an electronic nose (E-nose) were applied to detect and identify freeze-dried Agaricus bisporus infected with the five fungi.

RESULTS

Partial least squares regression (PLSR) models were used to distinguish the HSI spectra of the five fungi on the plates. The A. ochraceus group had the highest calibration performance: coefficient of calibration (R_c ² ) = 0.786, root mean-square error of calibration (RMSEC) = 0.125 log CFU g^-1 . The A. flavus group had the highest prediction performance: coefficient of prediction (R_p ² ) = 0.821, root mean-square error of prediction (RMSEP) = 0.083 log CFU g^-1 . The ratio of performance deviation (RPD) values of all of the models was higher than 2.0 for the NIR, MIR, and E-nose results for freeze-dried A. bisporus infected with different fungi. The fungal species and degree of infection can be distinguished by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using NIR, MIR, and E-nose, as the discrimination accuracy was more than 90%. The NIR methods had a higher recognition rate than the MIR and E-nose methods.

CONCLUSION

Principal component analysis (PCA) and PLSR models based on full spectra of HSI can achieve good discrimination results for these five fungi on plates. Moreover, NIR, MIR, and the E-nose were proven to be effective in monitoring fungal contamination on freeze-dried A. bisporus. However, NIR could be a more accurate method. © 2020 Society of Chemical Industry.

Gallium arsenide waveguides as a platform for direct mid-infrared vibrational spectroscopy

During recent years, mid-infrared (MIR) spectroscopy has matured into a versatile and powerful sensing tool for a wide variety of analytical sensing tasks. Attenuated total reflection (ATR) techniques have gained increased interest due to their potential to perform non-destructive sensing tasks close to real time. In ATR, the essential component is the sampling interface, i.e., the ATR waveguide and its material properties interfacing the sample with the evanescent field ensuring efficient photon-molecule interaction. Gallium arsenide (GaAs) is a versatile alternative material vs. commonly used ATR waveguide materials including but not limited to silicon, zinc selenide, and diamond. GaAs-based internal reflection elements (IREs) are a new generation of semiconductor-based waveguides and are herein used for the first time in direct spectroscopic applications combined with conventional Fourier transform infrared (FT-IR) spectroscopy. Next to the characterization of the ATR waveguide, exemplary surface reactions were monitored, and trace-level analyte detection via signal amplification taking advantage of surface-enhanced infrared absorption (SEIRA) effects was demonstrated. As an example of real-world relevance, the mycotoxin aflatoxin B1 (AFB1) was used as a model analyte in food and feed safety analysis.

High frequency modulation and (quasi) single-sideband emission of mid-infrared ring and ridge quantum cascade lasers

We investigate the high frequency modulation characteristics of mid-infrared surface-emitting ring and edge-emitting ridge quantum cascade lasers (QCLs). In particular, a detailed comparison between circular ring devices and ridge-QCLs from the same laser material, which have a linear waveguide in a "Fabry-Pérot (FP) type" cavity, reveals distinct similarities and differences. Both device types are single-mode emitting, based on either 2 ^nd- (ring-QCL) or 1 ^st-order (ridge-QCL) distributed feedback (DFB) gratings with an emission wavelength around 7.56 μm. Their modulation characteristics are investigated in the frequency-domain using an optical frequency-to-amplitude conversion technique based on the ro-vibrational absorptions of CH ₄. We observe that the amplitude of frequency tuning Δf over intensity modulation index m as function of the modulation frequency behaves similarly for both types of devices, while the ring-QCLs typically show higher values. The frequency-to-intensity modulation (FM-IM) phase shift shows a decrease starting from ∼72 ^∘ at a modulation frequency of 800 kHz to about 0 ^∘ at 160 MHz. In addition, we also observe a quasi single-sideband (qSSB) regime for modulation frequencies above 100 MHz, which is identified by a vanishing -1 ^st-order sideband for both devices. This special FM-state can be observed in DFB QCLs and is in strong contrast to the behavior of regular DFB diode lasers, which do not achieve any significant sideband suppression. By analyzing these important high frequency characteristics of ring-QCLs and comparing them to ridge DFB-QCLs, it shows the potential of intersubband devices for applications in e.g. novel spectroscopic techniques and highly-integrated and high-bitrate free-space data communication. In addition, the obtained results close an existing gap in literature for high frequency modulation characteristics of QCLs.

Machine learning algorithms for the automated classification of contaminated maize at regulatory limits via infrared attenuated total reflection spectroscopy

Mould infested maize poses a severe problem for farmers, food producers and for consumers worldwide. Mycotoxins are secondary metabolites produced by certain mould species that contaminate food and feed. The consumption of these toxins may cause serious health problems for humans and animals. The trichothecene deoxynivalenol (DON) constitutes one of the most commonly occurring Fusarium toxins encountered in maize and requires improved methods to limit its entrance into the food and feed system. While a variety of chromatographic and mass spectrometry methods for the identification of such toxins have been established, these are considered time-consuming, cost-intensive and require highly qualified personnel. Alternatively, optical techniques, such as mid-infrared spectroscopy offer rapid detection of fungal infections in cereals and other commodities with minimised sample preparation and analysis time. The present study demonstrates a rapid fungal contamination detection strategy in maize taking advantage of IR-spectroscopy combined with advanced machine learning algorithms. The developed method represents an advancement for the analysis of differences in protein and carbohydrate content revealed in the associated IR-spectra related to the amount of toxin contamination at the European Union (EU) regulatory limits for DON in maize (i.e. 1,250 μg/kg). The employed maize varieties are naturally infected samples or have been infected with Fusarium verticillioides, Fusarium graminearum or Fusarium culmorum. Sieved maize samples at the EU regulatory limit were correctly classified using machine learning approaches, therefore enabling the differentiation between DON-contaminated and non-contaminated maize samples. Specifically, a variety of machine learning methods, including Adaptive Boosting (AdaBoost), Random Forests, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) demonstrated excellent classification and validation performance using the obtained IR-spectra. As a result, 183 maize samples of different varieties and infection levels were accurately classified. 94% of the non-contaminated samples and 91% of the contaminated samples were correctly classified using an MLP classification approach.

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.

THz Spectroscopic Investigation of Wheat-Quality by Using Multi-Source Data Fusion

In order to improve the detection accuracy for the quality of wheat, a recognition method for wheat quality using the terahertz (THz) spectrum and multi-source information fusion technology is proposed. Through a combination of the absorption and the refractive index spectra of samples of normal, germinated, moldy, and worm-eaten wheat, support vector machine (SVM) and Dempster-Shafer (DS) evidence theory with different kernel functions were used to establish a classification fusion model for the multiple optical indexes of wheat. The results showed that the recognition rate of the fusion model for wheat samples can be as high as 96%. Furthermore, this approach was compared to the regression model based on single-spectrum analysis. The results indicate that the average recognition rates of fusion models for wheat can reach 90%, and the recognition rate of the SVM radial basis function (SVM-RBF) fusion model can reach 97.5%. The preliminary results indicated that THz-TDS combined with DS evidence theory analysis was suitable for the determination of the wheat quality with better detection accuracy.

Dimethyl fumarate reduces the risk of mycotoxins via improving intestinal barrier and microbiota

The effects of dimethyl fumarate (DMF) on mycotoxins and animal growth performance are well documented. However, its mechanism of anti-mildew effects is still unknown. The current study investigated how DMF detoxified the mycotoxin and improved the growth performance using BALB/c mice model, especially its effects on intestinal barrier function and gut micro-ecology. Our study also compared with the ultraviolet radiation (UR) treatment, a traditional anti-mildew control (TC). The results indicated that the DMF treatment had a lower contents of mycotoxin, better growth performance and improved mucosal morphology (P < 0.05), accompanied with the decreased intestinal permeability and the tighter gut barrier. Moreover, the efficiency of DMF was better than TC (P < 0.05). 16S rRNA gene sequence analysis revealed that the richness and diversity of bacteria was increased in DMF treatment. The most abundant OTUs belonged to Firmicutes and Bacteroidetes, and their changes in DMF were more moderate than the TC group, suggesting a more stable micro-ecology and the positive impact of DMF on the biodiversity of intestine. Specifically, the increased abundance of bacteria producing short-chain fatty acids (SCFAs), such as Gemella, Roseburia, Bacillus and Bacteroides in DMF group and prebiotics such as Lactobacillus in TC group, suggested a more healthier microbial composition and distribution. These findings supported that DMF had significant effects on animal's growth performance and intestinal barrier function by modulating the pathway of nutrient absorption and increasing the diversity and balance of gut microbes, which also illuminate that DMF is more efficient than traditional anti-mildew method.

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.

Sensitive Flow-through Immunoassay for Rapid Multiplex Determination of Cereal-borne Mycotoxins in Feed and Feed Ingredients

An easy-to-operate membrane-based flow-through test for multiplex screening of four mycotoxins (zearalenone, deoxynivalenol, aflatoxin B₁, and ochratoxin A) in a variety of cereal-based feed ingredients and compound feeds, such as wheat, barley, soybean, wheat bran, rice, rice bran, maize, rapeseed meal, and sunflower meal, and various types of complete feed (duckling feed, swine feed, broiler feed, piglet feed) was developed and validated. First, the antibodies were evaluated by enzyme-linked immunosorbent assay and then employed in the membrane rapid test. The cutoff levels for zearalenone, deoxynivalenol, aflatoxin B₁, and ochratoxin A were 50, 200, 1, and 10 μg/kg, respectively, based on European regulations and consumers' requirements. As sample pretreatment, consecutive steps of extraction, dilution, solid-phase extraction by addition of C18 sorbent, and final filtration of supernatant were followed. Both the sample preparation and the analysis procedure were simple, cost-effective, and easy to perform on-site in a nonlaboratory environment. The impact of sample processing on the result of the experiment was investigated supported by experimental design. The validation procedure was performed on the basis of Commission Regulation 2006/401/EC. The numbers of false-positive and false-negative outcomes were <5%, going along with the Commission Decision 2002/657/EC. Liquid chromatography-tandem mass spectrometry was performed as a confirmatory technique.

Portable Infrared Laser Spectroscopy for On-site Mycotoxin Analysis

Mycotoxins are toxic secondary metabolites of fungi that spoil food, and severely impact human health (e.g., causing cancer). Therefore, the rapid determination of mycotoxin contamination including deoxynivalenol and aflatoxin B1 in food and feed samples is of prime interest for commodity importers and processors. While chromatography-based techniques are well established in laboratory environments, only very few (i.e., mostly immunochemical) techniques exist enabling direct on-site analysis for traders and manufacturers. In this study, we present MYCOSPEC - an innovative approach for spectroscopic mycotoxin contamination analysis at EU regulatory limits for the first time utilizing mid-infrared tunable quantum cascade laser (QCL) spectroscopy. This analysis technique facilitates on-site mycotoxin analysis by combining QCL technology with GaAs/AlGaAs thin-film waveguides. Multivariate data mining strategies (i.e., principal component analysis) enabled the classification of deoxynivalenol-contaminated maize and wheat samples, and of aflatoxin B1 affected peanuts at EU regulatory limits of 1250 μg kg-1 and 8 μg kg-1, respectively.

Quantitative analysis of Sudan dye adulteration in paprika powder using FTIR spectroscopy

As adulteration of foodstuffs with Sudan dye, especially paprika- and chilli-containing products, has been reported with some frequency, this issue has become one focal point for addressing food safety. FTIR spectroscopy has been used extensively as an analytical method for quality control and safety determination for food products. Thus, the use of FTIR spectroscopy for rapid determination of Sudan dye in paprika powder was investigated in this study. A net analyte signal (NAS)-based methodology, named HLA/GO (hybrid linear analysis in the literature), was applied to FTIR spectral data to predict Sudan dye concentration. The calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results had a high determination coefficient (R²) of 0.98 and low root mean square error (RMSE) of 0.026% for the calibration set, and an R² of 0.97 and RMSE of 0.05% for the validation set. The model was further validated using a second validation set and through the figures of merit, such as sensitivity, selectivity, and limits of detection and quantification. The proposed technique of FTIR combined with HLA/GO is rapid, simple and low cost, making this approach advantageous when compared with the main alternative methods based on liquid chromatography (LC) techniques.

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.

Advances in Mid-Infrared Spectroscopy for Chemical Analysis

Infrared spectroscopy in the 3-20 μm spectral window has evolved from a routine laboratory technique into a state-of-the-art spectroscopy and sensing tool by benefitting from recent progress in increasingly sophisticated spectra acquisition techniques and advanced materials for generating, guiding, and detecting mid-infrared (MIR) radiation. Today, MIR spectroscopy provides molecular information with trace to ultratrace sensitivity, fast data acquisition rates, and high spectral resolution catering to demanding applications in bioanalytics, for example, and to improved routine analysis. In addition to advances in miniaturized device technology without sacrificing analytical performance, selected innovative applications for MIR spectroscopy ranging from process analysis to biotechnology and medical diagnostics are highlighted in this review.