Impact of the Temperature Reconditioning of Cold-Stored Potatoes on the Color of Potato Chips and French Fries

The effect of temperature reconditioning on cold-stored potato tubers was investigated for three popularly consumed potato varieties (Shangi, Unica, and Dutch robjin) grown in Kenya. The potatoes were stored at 4 °C for 30 days, followed by removal and storage at 22 ± 3 °C for 9 days during which changes in sugar concentration were evaluated every two days. In parallel, potato chips and French fries were processed, and their colors were determined. The results showed that sugar content decreased significantly with increasing reconditioning time. The relative decrease in fructose content was the highest (p < 0.05) in Dutch robjin (57.49%), followed by Shangi (49.22%) and Unica (38.18%). Glucose content decreased by 54.1% in Dutch robjin, 49.5% in Shangi, and 50.8% in Unica. The lightness (L*) of French fries and chips increased significantly (p < 0.05) with reconditioning time while the redness (a*) values decreased significantly (p < 0.05) across all varieties. The correlation between lightness and the total reducing sugar content of the potatoes was r < -0.93, indicating a strong negative correlation for both products. The coefficient of determination showed that the glucose content of the tubers accounted for 80.5-97.6% of the lightness of French fries and 88.4-94.2% for potato chips. The critical glucose content range for acceptable products in French fries and chips based on the color (L* and a*) values was 12-22 mg/100g and 8-14 mg/100g, respectively, for the varieties in this study.

Application of Machine Vision System in Food Detection

Food processing technology is an important part of modern life globally and will undoubtedly play an increasingly significant role in future development of industry. Food quality and safety are societal concerns, and food health is one of the most important aspects of food processing. However, ensuring food quality and safety is a complex process that necessitates huge investments in labor. Currently, machine vision system based image analysis is widely used in the food industry to monitor food quality, greatly assisting researchers and industry in improving food inspection efficiency. Meanwhile, the use of deep learning in machine vision has significantly improved food identification intelligence. This paper reviews the application of machine vision in food detection from the hardware and software of machine vision systems, introduces the current state of research on various forms of machine vision, and provides an outlook on the challenges that machine vision system faces.

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.

Advanced Analysis of Roots and Tubers by Hyperspectral Techniques

Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.

Review: computer vision applied to the inspection and quality control of fruits and vegetables

This is a review of the current existing literature concerning the inspection of fruits and vegetables with the application of computer vision, where the techniques most used to estimate various properties related to quality are analyzed. The objectives of the typical applications of such systems include the classification, quality estimation according to the internal and external characteristics, supervision of fruit processes during storage or the evaluation of experimental treatments. In general, computer vision systems do not only replace manual inspection, but can also improve their skills. In conclusion, computer vision systems are powerful tools for the automatic inspection of fruits and vegetables. In addition, the development of such systems adapted to the food industry is fundamental to achieve competitive advantages.