## 4 Application of HSI and AI in the Food Industry
### Liquid Products  
The versatility of the HSI-AI combination in analyzing liquid products offers non-destructive, real-time, and highly accurate solutions for quality assessment across various liquid food industries. A robust analysis methodology of the composition of alcohols and esters in Baijiu will be developed using HSI and machine learning. Applying PSO, Support Vector Regression and random forest models demonstrated high accuracy for ethanol-ester detection. Specific wavelengths in the NIR region allowed for the highly precise quantification of the compounds, with an Rp2 above 0.99. This means that when HSI processing is being carried out through AI-based methods, it has the potential to offer a non-destructive, accurate method of assessment of chemical composition as regards alcoholic beverages for quality control and confirmation of authenticity [163]. Since monitoring liquid products is important to preserve them from decontamination and mitigation, it is crucial to develop an HSI method to check the composition, consistently. Furthermore, in some cases like pasteurized milk, models can be developed to check the microorganism activities inside the milk in order to optimize the storage conditions. This was also carried out for the morphological and moisture content changes in drying droplets of Lonicerae Japonicae Flos extract. Partial least squares regression and artificial neural network analysis, which were integrated with hyperspectral data, enabled the noninvasive monitoring of drying kinetics. This method can be transferred to various other liquid food products, such as milk or fruit juice concentrates, in order to support food manufacturers in the elaboration of optimized processing methods that yield more uniform final products [164]. Another study tested oil adulteration detection by HSI through the use of various machine learning models, including logistic regression and linear discriminant analysis. This study could, hence, establish that HSI has the potential to be a very powerful technique in the detection of the nature of adulteration in oils at various stages of processing. The method is of wider applicability in liquid food products such as edible oils vis-a-vis their authenticity and protection of consumer health [165]. Moreover, this work may present complementary results and point out potential applications of both HSI and machine learning models in the optimization of drying processes for other products, like coffee extracts and plant-based beverages. The fact that this technology allows for the monitoring of dynamic changes in liquid foods with high accuracy makes it a potential tool for enhancing consistency and quality for a wide range of processed products. Further development dealing with integration of HSI into systems for real-time monitoring could further improve processing efficiency in the food industry, especially for liquid matrices of complicated composition. It can be inferred that the integration of HSI with AI has brought a revolution in the quality control and safety measures at all three phases of the food products, namely solid, solid-liquid, and liquid. The integration showed enormous benefits compared to conventional and statistical methods, as represented in Table 4. For solid products, such as fruits, grains, and meats, the HSI integrated with AI models improved the non-destructive evaluation quality parameters, including spoilage detection and identification of fraud, at an incredible accuracy. Coupling HSI with AI provides powerful solutions for liquid products regarding composition analysis, adulteration detection, and monitoring processing stages. This technological synergy leads to efficiency and accuracy, from real-time, non-invasive inspections to high standards of food safety and quality. In fact, these advances in HSI and AI applications hold quite immense potential for benefiting the entire food industry by making intelligent and dependable quality-control systems a feasible reality.