Predicting Aflatoxin contamination in white and yellow maize using Vis/NIR spectroscopy combined with PCA-LDA and PLSR Models through Aquaphotomics Approaches

Maize is considered one of the top three most consumed staples worldwide. Maize is a more versatile and multipurpose crop in comparison to wheat and rice, as a result, maize plays a diverse and dynamic role in global agri-food systems and contributes significantly to food and nutrition security (Ranum, Peña‐Rosas and Garcia‐Casal, 2014; Grote et al., 2021). Maize, consumed widely across the globe and it is used in the preparation of several staples.

However, it is highly susceptible to contamination during production, storage, and transportation. Aflatoxin infestation in maize caused by the fungi, Aspergillus flavus and Aspergillus parasiticus remains one of the biggest challenges facing the agricultural sector. Excessive consumption of aflatoxin-contaminated food has been linked to liver cancer, stunted growth in children, weakened immune systems, and poor birth outcomes.

The rate of aflatoxin infestation is increasing due to changes in the climate. These risks are compounded in rural communities where storage facilities are often inadequate, and food inspection systems are limited. A shift has recently been observed as a result of climate change, in the presence of aflatoxin producer Aspergillus spp. in Europe, with consequent aflatoxin contamination in agricultural commodities including maize in several European countries that have not faced with this problem before, including, e.g. Northern Italy, Serbia, Slovenia, Croatia and Romania (Dobolyi et al., 2013).

Near-infrared spectroscopy (NIRS) is a rapid, non-destructive, eco-friendly and accurate technique which requires little training in its operations. This technique has been applied in several studies to authenticate the quality of foods, ranging from quality assessment of honey based on the geographical regions to quality determination of frozen foods.  One of the most difficult challenges in aflatoxin management is the real-time monitoring, detection, and removal of contaminated food products throughout the value chain. Near-Infrared Spectroscopy (NIRS) plays an important role in this digitalization process, providing significant advantages over traditional automated monitoring techniques.

Coupled with chemometric techniques, NIRS offers insight into the chemical composition of foods. It helps to classify food products based on similarities in the samples and also to quantify and predict contaminants present in the foods.

This study uniquely incorporates aquaphotomics, a novel approach. Aquaphotomics focuses on water, as a complex molecular matrix as well as an integral part of any aqueous system. Water is sensitive to any change the system experiences, external or internal. As such, the molecular structure of water revealed through its interaction with light of all frequencies becomes a source of information about the state of the system, an integrative marker of system dynamics

This study aimed to develop models, optimized with pre-processing techniques and wavelength ranges to classify and predict 0, 3, 5, 10, 20, 30, and 50 ng/g aflatoxin in three major datasets (naturally contaminated white, spiked white maize, and spiked yellow maize). The models were validated by assessing the presence of aflatoxins in maize collected from markets in six major regions: Ashanti, Bono East, Eastern, Greater Accra, Northern, and Upper East. A total of 600 maize samples were analyzed using the  High-Performance Liquid Chromatography (HPLC) and the more accessible Near-Infrared Spectroscopy (NIRS) technique.

Absorption peaks and bands (500, 950,1000, 1300, 1500, 1900, 2100, and 2300nm) were observed in the spectra, which could be related to aflatoxin contamination. Using all three datasets, the highest classification accuracies of 92.52% and 92.54% were obtained when models were developed at the wavelength range of 450-1050nm and1150-2400nm with Savitsky Golay smoothing (first derivative with filter 17).

Sensitivity, precision, specificity, and F1 score close to 1. Classification accuracies were 100% at all the distinct wavelength ranges when models were developed separately for each dataset. Partial least squares regression yielded an R2CV of 0.99, RMSECV of 1.70 ng/g, RPD of 9.90, LOD of 0.60 ng/g, and LOQ of 1.81 ng/g at the wavelength range of 450-1050nm, indicative of model robustness and high performance.

Aquagrams revealed water matrix coordinates that could be related to aflatoxin presence in maize. The findings suggest that NIRS can be explored as a potential alternative approach for aflatoxin detection and quantification in maize.

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Recommendations

The study recommends that the use of NIRS techniques should be scaled up and integrated into national and global food safety monitoring frameworks. By deploying portable handheld NIRS devices in local markets and community storage centers, government agencies and NGOs can provide farmers and traders with immediate feedback on aflatoxin risks, thereby reducing the likelihood of contaminated food entering the food supply chain.

Moreover, increased awareness and early detection could prompt improvements in postharvest practices, such as proper drying, use of hermetic storage bags, and timely market delivery. The authors also recommend targeted interventions in regions with higher aflatoxin prevalence and training programs to build local capacity in using NIRS technology.

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