A concise review on food quality assessment using digital image processing

Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review

The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.

Region of interest selection in heterogeneous digital image: Wine age prediction by comprehensive two-dimensional gas chromatography

This study integrates genetic algorithm (GA) with partial least squares regression (PLSR) and various variable selection methods to identify impactful regions of interest (ROI) in heterogeneous 2D chromatogram images for predicting wine age. As wine quality and aroma evolve over time, transitioning from youthful fruitiness to mature, complex flavors, which leads to alterations in the composition of essential aroma-contributing compounds. Chromatograms are segmented into subimages, and the GA-PLSR algorithm optimizes combinations based on grayscale, red-green-blue (RGB), and hue-saturation-value (HSV) histograms. The selected subimage histograms are further refined through interval selection, highlighting the compounds with the most significant influence on wine aging. Experimental validation involving 38 wine samples demonstrates the effectiveness of this approach. Cross-validation reduces the PLS model error from 2.8 to 2.4 years within a 10 × 10 subset, and during prediction, the error decreases from 2.5 to 2.3 years. The study presents a novel approach utilizing the selection of ROI for efficient processing of 2D chromatograms focusing on predicting wine age.

In Situ Growth of Conductive Metal-Organic Framework onto Cu2O for Highly Selective and Humidity-Independent Hydrogen Sulfide Detection in Food Quality Assessment

The sensitivity of chemiresistive gas sensors based on metal oxide semiconductors (MOSs) has been inherently affected by ambient humidity because their reactive oxygen species are easily hydroxylated by water molecules, which significantly reduces the accuracy of the gas sensors in food quality assessment. Although conventional metal organic frameworks (MOFs) can serve as coatings for MOSs for humidity-independent gas detection, they have to operate at high working temperatures due to their low or nonconductivity, resulting in high power consumption, significant manufacturing inconvenience, and short-term stability due to the oxidation of MOFs. Here, the conductive and thickness-controlled CuHHTP (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene)-coated Cu2O are developed by combining in situ etching and layer-by-layer liquid-phase growth method, which achieves humidity-independent detection of H2S at room temperature. The response to H2S only decreases by 2.6% below 75% relative humidity (RH), showing a 9.6-fold improvement than the bare Cu2O sensor, which is ascribed to the fact that the CuHHTP layer hinders the adsorption of water molecules. Finally, a portable alarm system is developed to monitor food quality by tracking released H2S. Compared with gas chromatography method, their relative error is within 9.4%, indicating a great potential for food quality assessment.

Green and fast prediction of crude protein contents in bee pollen based on digital images combined with Random Forest algorithm

Bee pollen is considered an excellent dietary supplement with functional characteristics, and it has been employed in food and cosmetics formulations and in biomedical applications. Therefore, understanding its chemical composition, particularly crude protein contents, is essential to ensure its quality and industrial application. For the quantification of crude protein in bee pollen, this study explored the potential of combining digital image analysis and Random Forest algorithm for the development of a rapid, cost-effective, and environmentally friendly analytical methodology. Digital images of bee pollen samples (n = 244) were captured using a smartphone camera with controlled lighting. RGB channels intensities and color histograms were extracted using open source softwares. Crude protein contents were determined using the Kjeldahl method (reference) and in combination with RGB channels and color histograms data from digital images, they were used to generate a predictive model through the application of the Random Forest algorithm. The developed model exhibited good performance and predictive capability for crude protein analysis in bee pollen (R2 = 80.93 %; RMSE = 1.49 %; MAE = 1.26 %). Thus, the developed analytical methodology can be considered environmentally friendly according to the AGREE metric, making it an excellent alternative to conventional analysis methods. It avoids the use of toxic reagents and solvents, demonstrates energy efficiency, utilizes low-cost instrumentation, and it is robust and precise. These characteristics indicate its potential for easy implementation in routine analysis of crude protein in bee pollen samples in quality control laboratories.

A Reliable Method for Determining the Degree of Orientation of Fibrous Foods Using Laser Transmission and Computer Vision

The degree of organised alignment of fibre structures, referred to as the degree of orientation, significantly influences the textural properties and consumer acceptance of fibrous foods. To develop a new method to quantitatively characterise the fibre structure of such foods, a laser transmission imaging system is constructed to capture the laser beam spot on a sample, and the resulting image undergoes a series of image processing steps that use computer vision to translate the light and dark variations of the original images into distinct ellipses. The results show that the degree of orientation can be reasonably calculated from the ellipse obtained by fitting the outermost isopixel points. To validate the reliability of the newly developed method, we determine the degree of orientation of typical fibrous foods (extruded beef jerky, pork jerky, chicken jerky, and duck jerky). The ranking of the measured orientation agrees with the results of pseudocolour maps and micrographs, confirming the ability of the method to distinguish different fibrous foods. Furthermore, the relatively small coefficients of variation and the strong positive correlation between the degree of organisation and the degree of orientation confirm the reliability of this newly developed method.

Image-Based Sensor for Liquid Level Monitoring during Bottling with Application to Craft and Home-Brewing

Although craft and home brewing have fueled the beer renaissance in the last decade, affordable, reliable, and simple sensing equipment for such breweries is limited. Thus, this manuscript is motivated by the improvement of the bottle-filling process in such settings with the objective of developing a liquid level sensor based on a novel application of the known optical phenomena of light refraction. Based on the different refraction indices of liquid and air (and critical angle based on Snell's law), along with a novel LED light source positioning, a reliable liquid level sensor system was built with the aid of an embedded microcontroller. The used operating principle is general and can be used in applications other than the proposed one. The proposed method was extensively tested in a laboratory and limited production settings with a speed of 7 Hz using different liquids and container shapes. It was compared for accuracy to other sensing principles such as ultrasound, infrared, and time-of-flight. It demonstrated comparable or better performance with a height error ranging between -0.1534 mm in static conditions and 1.608 mm for realistic dynamic conditions and good repeatability on the production line with a 4.3 mm standard deviation of the mean.

Porosity determination of Vienna sausages through digital images analysis

BACKGROUND

This research aimed to determine the porosity and particle size distribution in canned Vienna-type sausages using digital image analysis (DIA) on photographs captured with a digital camera and applying a Monte Carlo simulation. The methodology determined morphometric parameters (area and Feret diameter) by DIA of transverse and longitudinal sections of canned sausages. Those images were previously contrast enhanced, color threshold adjusted, and binarized. Subsequently, the estimation of the pore volume was carried out from the inverse Gaussian distributions of Feret diameter and area, as well as the porosity, using Monte Carlo simulation.

RESULTS

The pores had an average Feret diameter of 0.335 mm and an average area of 0.085 mm² . The highest estimated bivariate kernel density was presented for the smallest pores (around 0.02 mm² in area and 0.25 mm in diameter). Simulation average values of pore volume, assumed as a cylinder, and porosity were 1.455 mm³ and 0.737 respectively. The average porosity value was consistent with the value experimentally estimated by the indirect method, in concordance with the definition of porosity, which was 0.715, presenting a mean relative percentage error of 3.08% concerning the estimated experimental value as well.

CONCLUSION

This research presents interesting perspectives for the quantitative analysis of the microstructure of food and biological materials through a novel, low-cost, reliable, and fast proposal. Moreover, this is the first study to report the porosity determination in canned sausages by DIA. © 2022 Society of Chemical Industry.

An Automated Image Processing Module for Quality Evaluation of Milled Rice

The paper demonstrates a low-cost rice quality assessment system based on image processing and machine learning (ML) algorithms. A Raspberry-Pi based image acquisition module was developed to extract the structural and geometric features from 3081 images of eight different varieties of rice grains. Based on features such as perimeter, area, solidity, roundness, compactness, and shape factor, an automatic identification system is developed to segment the grains based on their types and classify them by using seven machine learning algorithms. These ML models are trained using the images and are compared using different ML models. ROC curves are plotted for each model for quantitative analysis to assess the model’s performance. It is concluded that the random forest classifier presents an accuracy of 77 percent and is the best-performing model for the classification of rice varieties. Furthermore, the same algorithm is efficiently employed to determine the price of adulterated rice samples based upon the market price of individual rice.

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods. These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.

Multi-Response Optimization of Pyrrolizidine Alkaloids Removal from Chrysanthemum morifolium by High-Pressure Extraction

As an ingredient in various foods, Chrysanthemum morifolium flower is popular due to its multiple health benefits. Pyrrolizidine alkaloids (PAs) are hepatotoxic secondary metabolites in Chrysanthemum family. Effects of high-pressure extraction (HPE) on PAs removal efficiency, as well as the retention efficiency of functional components, including chlorogenic acid, luteolin-7-β-D-glucopyranoside, 3,5-dicaffeyl quinic acid and total flavonoids, were investigated and optimized using response surface methodology (RSM). Pressure (0.1-200 MPa), numbers of cycles (1-5) and acetic acid concentration (0-10%) were chosen as the independent variables. The results indicated that the pressure was the most significant factors affecting all responses. The optimum HPE for removing Pas and retaining functional components were set at 124 MPa, with one cycle and with an acetic acid concentration of 10%. After comparing the experimental optimum values and predicted optimum values, the validity of RSM model was proved.