Non-destructive detection of mycotoxins in maize kernels using diffuse reflectance spectroscopy

Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms

Fumonisins occurrence in maize represents a significant global challenge, impacting economic stability and food safety. This study evaluates the potential of near-infrared (NIR) spectroscopy combined with chemometric algorithms to detect fumonisins in maize. For fumonisin B1 (FB1) and B2 (FB2) levels were developed predictive NIR models using partial least squares (PLS) and artificial neural networks (ANN). PLS models demonstrated strong correlation coefficient (R2) values of 0.90 (FB1), 0.98 (FB2), and 0.91 (FB1 + FB2) for calibration, with ratio of prediction to deviation (RPD) values ranging 2.8-3.6. Similarly, ANN models showed good predictive performance, particularly for FB1 + FB2, with R = 0.99, and the root means square error (RMSE) of 131 μg/kg for calibration; and R = 0.95, RMSE = 656 μg/kg for validation. These findings underscore the efficacy of NIR spectroscopy as a rapid, non-destructive tool for fumonisin screening in maize, with chemometric algorithms enhancing model accuracy, offering a valuable method for ensuring food safety.

Array-optimized artificial olfactory sensor enabling cost-effective and non-destructive detection of mycotoxin-contaminated maize

The MobileNetV3-based improved sine-cosine algorithm (ISCA-MobileNetV3) was combined with an artificial olfactory sensor (AOS) to address the redundancy in olfactory arrays, thereby achieving low-cost and high-precision detection of mycotoxin-contaminated maize. Specifically, volatile organic compounds of maize interacted with unoptimized AOS containing eight porphyrins and eight dye-attached nanocomposites to obtain the scent fingerprints for constructing the initial data set. The optimal decision model was MobileNetV3, with more than 98.5% classification accuracy, and its output training loss would be input into the optimizer ISCA. Remarkably, the number of olfactory arrays was reduced from 16 to 6 by ISCA-MobileNetV3 with about a 1% decrease in classification accuracy. Additionally, the developed system showed that each online evaluation was less than one second on average, demonstrating outstanding real-time performance for ensuring food safety. Therefore, AOS combined with ISCA-MobileNetV3 will encourage the development of an affordable and on-site platform for maize quality detection.

Exploration of Volatileomics and Optical Properties of Fusarium graminearum-Contaminated Maize: An Application Basis for Low-Cost and Non-Destructive Detection

Fusarium graminearum (F. graminearum) in maize poses a threat to grain security. Current non-destructive detection methods face limited practical applications in grain quality detection. This study aims to understand the optical properties and volatileomics of F. graminearum-contaminated maize. Specifically, the transmission and reflection spectra (wavelength range of 200-1100 nm) were used to explore the optical properties of F. graminearum-contaminated maize. Volatile organic compounds (VOCs) of F. graminearum-contaminated maize were determined by headspace solid phase micro-extraction with gas chromatography-tandem mass spectrometry. The VOCs of normal maize were mainly alcohols and ketones, while the VOCs of severely contaminated maize became organic acids and alcohols. The ultraviolet excitation spectrum of maize showed a peak redshift as fungi grew, and the intensity decreased in the 400-600 nm band. Peak redshift and intensity changes were observed in the visible/near-infrared reflectance and transmission spectra of F. graminearum-contaminated maize. Remarkably, optical imaging platforms based on optical properties were developed to ensure high-throughput detection for single-kernel maize. The developed imaging platform could achieve more than 80% classification accuracy, whereas asymmetric polarization imaging achieved more than 93% prediction accuracy. Overall, these results can provide theoretical support for the cost-effective preparation of low-cost gas sensors and high-prediction sorting equipment for maize quality detection.

UV reflectance in crop remote sensing: Assessing the current state of knowledge and extending research with strawberry cultivars

Remote sensing of spectral reflectance is a crucial parameter in precision agriculture. In particular, the visual color produced from reflected light can be used to determine plant health (VIS-IR) or attract pollinators (Near-UV). However, the UV spectral reflectance studies largely focus on non-crop plants, even though they provide essential information for plant-pollinator interactions. This literature review presents an overview of UV-reflectance in crops, identifies gaps in the literature, and contributes new data based on strawberry cultivars. The study found that most crop spectral reflectance studies relied on lab-based methodologies and examined a wide spectral range (Near UV to IR). Moreover, the plant family distribution largely mirrored global food market trends. Through a spectral comparison of white flowering strawberry cultivars, this study discovered visual differences for pollinators in the Near UV and Blue ranges. The variation in pollinator visibility within strawberry cultivars underscores the importance of considering UV spectral reflectance when developing new crop breeding lines and managing pollinator preferences in agricultural fields.

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.

Is chemical analysis suitable for detecting mycotoxins in agricultural commodities and foodstuffs?

The assessment of the risks of mycotoxins to humans through consuming contaminated foods resulted in specific legislation that evaluates the presence, quantities, and type of mycotoxins in agricultural commodities and foodstuffs. Thus, to ensure compliance with legislation, food safety and consumer health, the development of suitable analytical procedures for identifying and quantifying mycotoxins in the free or modified form, in low-concentration and in complex samples is necessary. This review reports the application of the modern chemical methods of analysis employed in mycotoxin detection in agricultural commodities and foodstuffs. It is reported extraction methods with reasonable accuracy and those present characteristics according to guidelines of Green Analytical Chemistry. Recent trends in mycotoxins detection using analytical techniques are presented and discussed, evaluating the robustness, precision, accuracy, sensitivity, and selectivity in the detection of different classes of mycotoxins. Sensitivity coming from modern chromatographic techniques allows the detection of very low concentrations of mycotoxins in complex samples. However, it is essential the development of more green, fast and more suitable accuracy extraction methods for mycotoxins, which agricultural commodities producers could use. Despite the high number of research reporting the use of chemically modified voltammetric sensors, mycotoxins detection still has limitations due to the low selectivity from similar chemical structures of mycotoxins. Furthermore, spectroscopic techniques are rarely employed due to the limited number of reference standards for calibration procedures.

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.

Prevalent Mycotoxins in Animal Feed: Occurrence and Analytical Methods

Today, we have been witnessing a steady tendency in the increase of global demand for maize, wheat, soybeans, and their products due to the steady growth and strengthening of the livestock industry. Thus, animal feed safety has gradually become more important, with mycotoxins representing one of the most significant hazards. Mycotoxins comprise different classes of secondary metabolites of molds. With regard to animal feed, aflatoxins, fumonisins, ochratoxins, trichothecenes, and zearalenone are the more prevalent ones. In this review, several constraints posed by these contaminants at economical and commercial levels will be discussed, along with the legislation established in the European Union to restrict mycotoxins levels in animal feed. In addition, the occurrence of legislated mycotoxins in raw materials and their by-products for the feeds of interest, as well as in the feeds, will be reviewed. Finally, an overview of the different sample pretreatment and detection techniques reported for mycotoxin analysis will be presented, the main weaknesses of current methods will be highlighted.