UV excited fluorescence image-based non-destructive method for early detection of strawberry (Fragaria × ananassa) spoilage

Non‐destructive prediction of TVB‐N using color‐texture features of UV‐induced fluorescence image for freeze–thaw treated frozen‐whole‐round tilapia

“…The cycle with higher TVB-N content had a deeper blue ROI image, which may relate to the chemical changes of small molecular dyes that could produce fluorescence reflection in tilapia scales during F-T cycles. 21 Moreover, the image contains a lot of deep information, including color, brightness and texture, and so forth. Therefore, it was necessary to build a machine learning model to determine the relationship between the complex features and TVB-N content.…”

“…18,19 Fluorescence is the phenomenon where a functional group in a molecule called a fluorescent compound (fluorophore) absorbs energy and excites at a specific wavelength. 20 A number of studies have demonstrated that the fluorescence phenomenon has great potential in the field of food detection, such as in studies by Huang et al 21 who used UV-induced fluorescence images to realize non-destructive detection of strawberries at the early stage of spoilage, Everard et al 22 who detected contaminants on spinach leaves by fluorescence imaging techniques and Zhuang et al 23 who realized real-time non-destructive detection of pork freshness using UV-induced fluorescence imaging technology. There were also specific applications applying the phenomenon to link the TVB-N value of fish freshness with that of the induced fluorescence image characteristics on the surface of FWRT.…”

Toward commercial applications of LED and laser‐induced fluorescence techniques for food identity, quality, and safety monitoring: A review

“…For the detection of tomatoes, images from the throat‐end area of tomatoes, and the whole area, except for the distal area, were defined as the ROI of the fluorescence and color images (Konagaya et al., 2020). For the detection of strawberries, images of strawberries were captured from the bottom and side views (Huang et al., 2022). The bottom views of the fluorescence images were used to discriminate healthy strawberries from those prone to rapid spoilage, because the bottom part of a strawberry was found to contain more fluorescent compounds according to EEM analysis.…”

“…Fatchurrahman et al (2020b) and Delwiche et al (2019) used LED at 365 nm to obtain chlorophyll fluorescence of mature and immature green tomatoes and sliced pepper rings, respectively, whereas Zhang et al (2014) used LED at 385 nm to acquire the fluorescence radiation of nectarine, mango, papaya, and guava fruits. With regard to samples with red surface, including tomato and strawberry, freshness (maturity) evaluation was based on the fluorescence emission of carotenoids, phenolic compounds, etc., that was excited by 365 nm LEDs (Huang et al, 2022;Konagaya et al, 2020). These studies were conducted on fluorescence and color imaging systems.…”

Quantitative and qualitative prediction of sulfur content in diesel by near infrared spectroscopy

“…Traditional methods for determining the sulfur content in diesel mainly include X-ray fluorescence spectroscopy, 9 lamp burning 10 and UV fluorescence. 11 Evaluation of sulfur content by X-ray fluorescence spectroscopy involves determining the intensity and wavelength of characteristic X-ray spectrum of sulfur. The main drawbacks of this method include high cost of operation and vulnerability to inference by signals from other compounds.…”