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This e-book (purchase required) covers the most common non-destructive methods used in food quality and authenticity analysis, including machine vision, Spectroscopy, E-nose/tongue, Ultrasonics, and hyperspectral imaging.. While these methods have been in practice for some time, the technological advancements of the last decade have improved the precision and reliability of these tools, making them more popular. 

The book intends to be a research volume giving an overview of the dominant non-destructive methods, including the more novel technologies such as biosensors and terahertz application.  It brings together detailed information on all these most current advances in technology and elucidates their application in food processing. It covers theory, principle, recent advances and practical applications in food analysis.  The book is aimed at students, researchers, food trainers and industry personnel.

The chapters all focus on applications in food analysis

• Spectrtoscopy: Optical Methods. Visible, NIR, FTIR
• NMR
• Computer vision systems
• X-Ray, CT and MRI
• Hyperspectral imaging
• Multispectral imaging
• Backscattering imaging
• Biospeckly imaging
• Thermal imaging
• Terahertz spectroscopy
• Ultrasonics
• Electronic nose and electronic tongue
• Biosensors
• Techniques based on electrical properties of food
• Colour and texture measurements
• AI and Machine Learning
• Back matter

This review (open access) provides an evaluation of microwave‐based systems (MW) in food applications, integrating both theoretical foundations and practical implementations. The fundamental principles of MW technology, including its theoretical background, sensing mechanisms, and imaging techniques, are discussed. The review then explores the applications of MW sensing and imaging in food analysis, encompassing contamination detection, moisture content evaluation, adulteration detection, quality control, and compositional assessment.

The advantages and limitations of MW systems for food applications are critically analyzed, along with an overview of commercial MW‐based technologies, relevant patent developments, and ongoing international research initiatives.

Finally, the future potential of MWS and MWI in the food industry is discussed, emphasizing their role in advancing real‐time, non‐invasive quality monitoring and strengthening food integrity.

This comprehensive review (open access) covers methods such as near-infrared spectroscopy (NIR), Fourier-transform near-infrared spectroscopy (FT-NIR), mid-infrared spectroscopy (MIR), ultraviolet-visible spectroscopy (UV-Vis), Raman spectroscopy, laser-induced breakdown spectroscopy (LIBS), hyperspectral imaging (HSI), and digital and thermal imaging techniques.

The authors consider that HSI and other imaging systems are best suited for solid samples measured in reflectance mode. These techniques are ideal for analyzing products like eggs, meat, fish, seafood, and milk powder. On the other hand, spectroscopy methods such as Raman, NIR, and FTIR spectroscopy can be adapted for both liquid (e.g., milk) and solid samples. These methods allow measurements in reflectance, transmittance, or absorbance modes.

Spectroscopic methods provide detailed chemical composition analysis for precise identification of changes in food samples that could signal loss of freshness or adulteration. However, detailed preprocessing steps are required, and some methods, like FTIR and NIR, are affected by scattering phenomena in turbid samples. In contrast, HSI and other imaging systems are highly effective for providing spatial information. This makes them valuable for visualizing structural differences, such as changes in surface texture or temperature caused by microbial activity, improper storage, or the presence of adulterants.

The authors consider that digital imaging is the most cost-effective method, making it accessible for routine inspections. However, it requires good lighting and environmental conditions for optimal results. Additionally, digital imaging is limited to surface-level analysis and cannot detect internal defects, such as egg freshness. For such applications, thermal imaging is required, though it comes at an additional cost.

Denaturation, spoilage, or adulteration can impact animal protein-based food quality and cause changes in protein conformation and composition, as well as high absorbance and reflectance signals.

CNN-based models can further automate the extraction of high-level features from images. In cases where limited datasets are available, data augmentation techniques, such as rotation, flipping, and scaling, are employed to increase dataset diversity and improve model performance. Additionally, resampling techniques like SMOTE can be applied to address class imbalances by generating synthetic samples of minority classes, enhancing model predictability without overfitting.

Often, selecting the optimal Machine Learning and modeling approach is not straightforward, leading to the application of multiple methods to achieve the desired analytical outcome.  Models may perform poorly on new data due to model complexity, sample size and effect size. K-fold cross-validation is a common approach used in the studies reviewed in this paper. However, K-fold cross-validation assumes data point independence, which can lead to variability in results across different data splits. To mitigate this limitation, techniques such as stratified K-fold cross-validation  or Leave-One-Out cross-validation can enhance model generalizability. Similarly, mechanisms like ECA, LRN, conjugate gradient, and sequential minimal optimization methods can be applied to improve the robustness and generalizability of CNN-based models.

Photo by Victoria Shes on Unsplash

In this study (purchase required) a machine vision system was used to capture the images of saffron samples at different safflower mixture proportions. Then three feature extraction algorithms - gray level co-occurrence matrix, gray-level run-length matrix, and Local Binary Pattern -  were applied to extract the textural features of data. Discriminant Analysis, Support Vector Machine, and Artificial Neural Network algorithms as supervised classification models were applied to classify datasets.

The models were applied for 3 class and 6 class datasets to explore classification ability. The best outcome for the 6-class dataset was with the Support Vector Machine model and with all features with an accuracy of 80 %. For 3 class datasets, Discriminant Analysis model had the best result with all features and with the accuracy of 97.78 %.

To explore the statistical importance of different features, two Minimum Redundancy Maximum Relevance and Chi-Square Test algorithms were applied. For the gray level co-occurrence matrix extracted features, Chi-Square Test algorithm with 10 features had the best accuracy with a test accuracy of 76.94 %.

The authors conclude that the proposed approach could be utilized in designing a system for checking saffron authenticity at a business-to-business point of sale..

Dehydrated bee pollen is a premium product with a growing market as a food supplement, functional food ingredient, and is also an ingredient in biomedical and healthcare formulations.  Pollen from certain species of stingless bees is higher priced than the equivalent from the much more common honey bee, as the former is considered to have enhanced nutritional and health benefits.  Pollens from different bee species are visually identical, giving an incentive for potential fraud.

In this paper (purchase required) the authors built a classification model to discriminate pollens from different bee species.  The researchers integrated digital image processing and machine learning to classify pollen loads produced by honey bees (Apis mellifera) and pot-pollen from stingless bee species based on their colour patterns. A total of 246 pollen loads and pot-pollen samples from five bee species (Apis mellifera, Melipona marginata, Melipona quadrifasciata quadrifasciata, Scaptotrigona bipunctata, and Tetragona clavipes) were collected, and high-resolution images were captured using a smartphone. Colour parameters extracted from images such as R, B, H, and V were analyzed, and classification models employing CatBoost, XGBoost, Random Forest, and k-Nearest Neighbors (kNN) algorithms were tested.

They reported that CatBoost achieved the highest performance, with accuracies of 100 % in the training phase and 98.9 % in the testing phase. Linear Discriminant Analysis (LDA) further validated the classification by grouping the pollen samples into five distinct clusters corresponding to the bee species studied.

They conclude that combining digital image processing from a smartphone with machine learning offers an effective approach to classifying pollen from different bee species, with promising applications in apiculture to ensure product quality and authenticity.

Photo by Aaron Burden on Unsplash

In this paper (open access) the authors demonstrate an optical, contactless method to discriminate different types of commercial milk (whole, partially skimmed and skimmed) and identify its adulteration with water and 12.5% water-glucose solution.  This adulterant was selected since it exhibits a refractive index comparable to that of whole milk, rendering such adulteration unnnoticed when performing a routine quality test based on refractive index measurements.

The prototype sensor employs a CMOS digital camera to acquire speckle pattern images generated by shining the beam of a red semiconductor laser onto milk samples placed in a plastic cuvette. The collected data are then analyzed to extract informative parameters, such as the average intensity and the speckle grain size.

The authors report that the system can distinguish between different types of milk and detect diluted samples with both water and glucose.