The rapid growth of the human population, the increase in consumer needs regarding food authenticity, and the sub-par synchronization between agricultural and food industry production necessitate the development of reliable track and tracing solutions for food commodities. The present research proposes a simple and affordable digital system that could be implemented in most production processes to improve transparency and productivity. The system combines non-destructive, rapid quality assessment methods, such as near infrared spectroscopy (NIRS) and computer/machine vision (CV/MV), with track and tracing functionalities revolving around the Internet of Things (IoT) and radio frequency identification (RFID). Meanwhile, authenticity is provided by a self-developed blockchain-based solution that validates all data and documentation “from farm to fork”. The system is introduced by taking certified Hungarian sweet potato production as a model scenario. Each element of the proposed system is discussed in detail individually and as a part of an integrated system, capable of automatizing most production flows while maintaining complete transparency and compliance with authority requirements. The results include the data and trust model of the system with sequence diagrams simulating the interactions between participants. The study lays the groundwork for future research and industrial applications combining digital tools to improve the productivity and authenticity of the agri-food industry, potentially increasing the level of trust between participants, most importantly for the consumers.

Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM—micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds—namely melamine, urea, taurine, and glycine—resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R²P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders.

Grape seed extract (GSE), one of the world’s bestselling dietary supplements, is prone to frequent adulteration with chemically similar compounds. These frauds can go unnoticed within the supply chain due to the use of unspecific standard analytical methods for quality control. This research aims to develop a near-infrared spectroscopy (NIRS) method for the rapid and non-destructive quantitative evaluation of GSE powder in the presence of multiple additives. Samples were prepared by mixing GSE with pine bark extract (PBE) and green tea extract (GTE) on different levels between 0.5 and 13% in singular and dual combinations. Measurements were performed with a desktop and three different handheld devices for performance comparison. Following spectral pretreatment, partial least squares regression (PLSR) and support vector regression (SVR)-based quantitative models were built to predict extract concentrations and various chemical parameters. Cross- and external-validated models could reach a minimum R²_p value of 0.99 and maximum RMSEP of 0.27% for the prediction of extract concentrations using benchtop data, while models based on handheld data could reach comparably good results, especially for GTE, caffeic acid and procyanidin content prediction. This research shows the potential applicability of NIRS coupled with chemometrics as an alternate, rapid and accurate quality evaluation tool for GSE-based supplement mixtures.

The processing of beans begins with a particularly time-consuming procedure, the hydration of the seeds. Ultrasonic treatment (US) represents a potential environmentally friendly method for process acceleration, while near-infrared spectroscopy (NIR) is a proposedly suitable non-invasive monitoring tool to assess compositional changes. Our aim was to examine the hydration process of red kidney beans of varying sizes and origins. Despite the varying surface areas, the beans’ soaking times of 13–15, 15–17, and 17–19 mm did not reveal significant differences between any of the groups (control; low power: 180 W, 20 kHz; high power: 300 W, 40 kHz). US treatment was observed to result in the release of greater quantities of water-soluble components from the beans. This was evidenced by the darkening of the soaking water’s color, the increase in the a* color parameter, and the rise in the dry matter value. NIRs, in combination with chemometric tools, are an effective tool for predicting the characteristics of bean-soaking water. The PLSR- and SVR-based modelling for dry matter content and light color parameters demonstrated robust model fits with cross and test set-validated R² values (>0.95), suggesting that these techniques can effectively capture the chemical information of the samples.

Milk powders are becoming a major attraction for many industrial applications due to their nutritional and functional properties. Different types of powdered milk, each with their own distinct chemical compositions, can have different functionalities. Consequently, the development of rapid monitoring methods is becoming an urgent task to explore and expand their applicability. Lately, there is growing emphasis on the potential of near-infrared spectroscopy (NIRS) as a rapid technique for the quality assessment of dairy products. In the present work, we explored the potential of NIRS coupled with chemometrics for the prediction of the main functional and chemical properties of three types of milk powders, as well as their important processing parameters. Mare, camel and cow milk powders were prepared at different concentrations (5%, 10% and 12%) and temperatures (25 °C, 40 °C and 65 °C), and then their main physicochemical attributes and NIRS spectra were analyzed. Overall, high accuracy in both recognition and prediction based on type, concentration and temperature was achieved by NIRS-based models, and the quantification of quality attributes (pH, viscosity, dry matter content, fat content, conductivity and individual amino acid content) also resulted in high accuracy in the models. R²CV and R²pr values ranging from 0.8 to 0.99 and 0.7 to 0.98, respectively, were obtained by using PLSR models. However, SVR models achieved higher R²CV and R²pr values, ranging from 0.91 to 0.99 and 0.80 to 0.99, respectively.

The agri-food industry faces increasing challenges to serve a rapidly growing population amidst intense climate change. Precision agriculture in the era of the 4th industrial revolution relies on digital data that is quickly and reliably generated. Unlike laboratory-bound wet chemistry methods, portable near infrared spectroscopy (NIRS) could provide rapid, non-destructive and on-the-spot assessment of crops with increasing regional importance, such as sweet potatoes. The present study aimed at developing an industrially feasible method by combining the NIR spectra of intact sweet potato samples and chemometrics to predict sweet potato characteristics. Samples from three sweet potato cultivars were stored at three different storage temperatures for one month with eight sampling occasions. Moisture content, total phenolic and total starch content were assessed to serve as reference. Spectra of tubers were recorded with a benchtop and a portable device with different arrangements: through-skin (intact), on cut-surface and on powdered samples. Models to differentiate between cultivars, storage conditions and predict reference parameters were built using principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares regression (PLSR) with rigorous cross- and test-set validation. Feasible regression models with up to 0.8 R2P could only be reached by using benchtop spectral data on powdered samples. Classification models, however, could reach 79.25% correct prediction to separate sweet potato cultivars by using the handheld spectral data recorded on the surface of intact tubers. The present results imply that handheld NIRS in combination with chemometrics could reach industrially feasible models to rapidly and nondestructively identify sweet potato cultivars, even those that show high physical similarities. The method presented here could be integrated into track and tracing systems where on-the-spot evaluations are necessary to ascertain product identity throughout the entire supply chain.