In today’s era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

Rapid determination of chemical compositions in chicken flesh by mining hyperspectral data

Nondestructive testing and visualization of compound heavy metals in lettuce leaves using fluorescence hyperspectral imaging.

Abstract Over the past two decades, hyperspectral imaging has become popular for non-destructive assessment of food quality, safety, and crop monitoring. Imaging delivers spatial information to complement the spectral information provided by spectroscopy. The key challenge with hyperspectral image data is the high dimensionality. Each image captures hundreds of wavelength bands. Reducing the number of wavelengths to an optimal subset is essential for speed and robustness due to the high multicollinearity between bands. However, there is yet to be a consensus on the best methods to find optimal subsets of wavelengths to predict attributes of samples. A systematic review procedure was developed and applied to review published research on hyperspectral imaging and wavelength selection. The review population included studies from all disciplines retrieved from the Scopus database that provided empirical results from hyperspectral images and applied wavelength selection. We found that 799 studies satisfied the defined inclusion criteria and investigated trends in their study design, wavelength selection, and machine learning techniques. For further analysis, we considered a subset of 71 studies published in English that incorporated spatial/texture features to understand how previous works combined spatial features with wavelength selection. This review ranks the wavelength selection techniques from each study to generate a table of the comparative performance of each selection method. Based on these findings, we suggest that future studies include spatial feature extraction methods to improve the predictive performance and compare them to a broader range of wavelength selection techniques, especially when proposing novel methods. 

Wavelength and texture feature selection for hyperspectral imaging: a systematic literature review

Infrared (IR) spectroscopy refers to the measurement of absorption of different frequencies of infrared radiations by foods or solids, liquids, and gases placed in the path of an infrared beam. The near and mid-IR regions of the spectrum are most useful for qualitative and quantitative analysis of foods. Fourier transform infrared (FTIR) spectroscopy has become an effective alternative to the traditional method of analysis. A total of 1685 records were collected through Scopus and 85 were included. This systematic review describes FTIR spectroscopy as highly desirable for the detection of adulterants, contaminants and toxins in various dairy products with large numbers of samples resulting in a high throughput speed, minimal sample preparation, ease of operation, low variable cost, and avoidance of hazardous chemicals. Use of chemometrics tools is a very effective approach to extract quantitative and qualitative information from FTIR spectra and to avoid food fraud. This study emphasis the application of FTIR in milk and other dairy products. This systematic review will give an insight to explore the application of FTIR mainly to industry and researchers. 

Application of FTIR spectroscopy in dairy products: a systematic review

Hyperspectral imaging combined with chemometrics for rapid detection of talcum powder adulterated in wheat flour

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Hyperspectral imaging combined with chemometrics for rapid detection of talcum powder adulterated in wheat flour