# Electronic Sensing Technologies in Food Quality Assessment: A Comprehensive Literature Review
## 6 e-Eye
The lighting can be adjusted for two construction types: circular lighting works best for flat samples, while diffuse lighting is more suitable for round and/or reflective objects [6]. Computer vision equipment utilizes sensors to mimic the human visual system. Using advanced image acquisition devices, relevant sample data are collected and sent to a computer to analyze and identify target areas. This technology enables the detection and sorting of fresh food. Computer vision equipment consists of two main components: the information processing device and the image acquisition device [169]. As computer vision technology develops, many computer vision devices are being created. This technology has numerous applications in food research, including quality control and classifying fruits and vegetables [58]. The computer vision system is fast, non-invasive, and highly precise. Compared to traditional testing equipment, this new technology significantly enhances the efficiency of food testing. Computer vision offers several advantages, including automatic classification and detection and economic and hygienic benefits. Additionally, it eliminates the influence of personal biases. However, there can be challenges in practical applications. Image acquisition devices, such as smart cameras, are sensitive to environmental factors like lighting, \begin{table}
\begin{tabular}{c c c c} \hline
**Device Type/Sensors Used** & **Application** & **Quantitative Metrics** & **References** \\ \hline The e-eye system consists of (1) a & Combined use of VE and EE & The detection efficiency of the & \\
5 million electronic eyepiece & to identify the storage time of & intelligent sensor system has been & \\ (RuiHoge), (2) a holder, (3) an LED lamp, and (4) an LED lamp adapter & Pu-erh tea & improved compared to conventional & [164] \\ \hline W100 wine color analyzer (Hanon Advanced Technology Group Co., Ltd., Jinan, China) & L\({}^{\star}\), a\({}^{\star}\), b\({}^{\star}\), C\({}^{\star}\), and h of & The results of the e-nose project showed & \\ \hline Color digital camera (Firewire Scion 1394 camera; Scion Corporation, Frederick, MD, USA) with maximum resolution (1600 \(\times\) 1200 pixels) in jpeg format, illumination by two lamps (23 W/865, Philips MASTER PL-Electronic) placed at an angle of 45\({}^{\circ}\) & Use of electronic sensors to assess the effect of different thermal profiles of water & Higher brewing temperatures resulted in greater foaming and greater foam stability & [166] \\ \hline Aparat E-eye (IRIS VA400, Alpha MOS, Françja) & Study of the color change of the Saffron floral bio-residues (SFB) sample under different storage conditions & It has been suggested that SFB should be stored at 25 \({}^{\circ}\)C with 23\% & [167] \\ \hline SC-80C colorimeter (Kangguang Instrument Co. Ltd., Beijing, China) & & Yellow large tea was significantly different from yellow bud teas and yellow small teas, but yellow bud teas & \\ in transmission mode under CIE D65/10\({}^{\circ}\)/observer illumination conditions & & & \\ \hline Abbreviations: L\({}^{\star}\)—lightness; a\({}^{\star}\)—green/red component; b\({}^{\star}\)—yellow/blue component, C\({}^{\star}\)—chrominance; h—hue. & & \\ \end{tabular}
\end{table} 
Table 5: _Cont._weather conditions, and high humidity. As a result, the images captured may vary in color brightness, become unstable, or exhibit other issues, which can lead to misinterpretation of the observed images [170]. Preprocessing the original image to enhance the final image analysis is known as low-level processing. This includes adjusting brightness and colors, cropping images to focus on the region of interest, and removing noise or digital artifacts caused by low light levels. Mid-level processing involves image segmentation, which separates the target object from unwanted information to improve accuracy. The most advanced stage is high-level processing, encompassing image recognition and interpretation.