Honey adulteration is a worldwide problem; however, its detection is a challenge for researchers and authorities. There are numerous ways of honey counterfeiting; amongst them, direct adulteration is one of the most common methods. Correlative techniques, such as near-infrared spectroscopy (NIRS), are useful tools in the detection of honey adulteration; however, this method has not been applied to Hungarian honeys. The aim of this research was to investigate the performance of NIRS for the detection of sugar syrup addition to Hungarian honeys at lower concentration levels (2.2 RPDCV value.

The quality of probiotics has been associated with bacteria and yeast strains’ contents and their stability against conditioning factors. Near-infrared spectroscopy (NIRS), as a non-destructive, fast, real-time, and cost-effective analytical technique, can provide some advantages over more traditional food quality control methods in quality evaluation. The aim of our study was to evaluate the applicability of NIRS to the characterization and viability prediction of three commercial probiotic food supplement powders containing lactic acid bacteria (LAB) subjected to concentration and temperature conditioning factors. For each probiotic, 3 different concentrations were considered, and besides normal preparation (25 °C, control), samples were subjected to heat treatment at 60 or 90 °C and left to cool down until reaching room temperature prior to further analysis. Overall, after applying chemometrics to the NIR spectra, the obtained principal component analysis-based linear discriminant analysis (PCA-LDA) classification models showed a high accuracy in both recognition and prediction. The temperature has an important impact on the discrimination of samples. According to the concentration, the best models were identified for the 90 °C temperature treatment, reaching 100% average correct classification for recognition and over 90% for prediction. However, the prediction accuracy decreased substantially at lower temperatures. For the 25 °C temperature treatment, the prediction accuracy decreased to nearly 60% for 2 of the 3 probiotics. Moreover, according to the temperature level, both the recognition and prediction accuracies were close to 100%. Additionally, the partial least square regression (PLSR) model achieved respectable values for the prediction of the colony-forming units (log CFU/g) of the probiotic samples, with a determination coefficient for prediction (R²Pr) of 0.82 and root mean square error for prediction (RMSEP) of 0.64. The results of our study show that NIRS is a fast, reliable, and promising alternative to the conventional microbiology technique for the characterization and prediction of the viability of probiotic supplement drink preparations.

Goat milk is a vital, sustainable dairy product source that contributes significantly to the global dairy market. Different goat breeds may produce milk with varying compositions during different lactation seasons. Therefore, milk composition and quality need to be monitored. As a versatile and non-destructive method, Near Infrared Spectroscopy (NIRS) offers the potential for evaluating various milk parameters. In this study, we aim to establish NIRS models for classifying goat’s milk samples between the native Red and Alpine Breed at first and fifth lactation season. Our objective is to assess the efficacy and cost-effectiveness of NIRS as a viable alternative to traditional laboratory techniques. Forty-five milk samples were collected from two breeds, French Alpine goats, and native Red goats. Within each breed, half of the samples originated from goats in their first lactation season and the other half from goats in their fifth lactation season. The NIRS measurements were conducted using homogenized, untreated, and diluted milk samples. The experiment was performed with a benchtop spectrophotometer (740–1700 nm). Qualitative analysis using linear discriminant analysis (LDA) was conducted, demonstrating the feasibility of NIRS in effectively classifying samples. The achieved accuracy ranged between 67.19% to 100%, depending on the breed and lactation period. These findings underscore the potential of NIRS as a rapid, non-destructive analytical tool for quality monitoring and milk analysis. Its insights guide decision-making in nutrition, agriculture, food production, and public health. By exploiting on the distinct attributes of goat milk originating from various breeds at different lactation periods, we can offer a wide range of appropriate and varied food choices tailored to distinct population groups.