
The food industry is looking for new techniques that allow rapid and large-scale determination of food quality parameters for application in automation control systems in industrial processes, as these automated systems facilitate real-time data collection. Among these techniques, instrumental methods based on molecular or vibrational spectroscopy stand out. The use of infrared spectra, either with spectrometers or with imaging systems, is suitable for in-line, on-line, or at-line analysis at different industrial process locations. In this webinar, we will have the honour of having a senior researcher and two junior researchers, all three experts in the field. First, Prof. Dr. Chao-Hui Feng will talk about the use of hyperspectral imaging and terahertz spectroscopy for food quality determination. Next, Mr. Fangchen Ding will talk about the non-invasive prediction of quality traits in packaged mango using near-infrared spectroscopy. Finally, Mr. Changzhou Zuo will elaborate on the previous topic, and explain how near-infrared spectroscopy can detect infected mango tissues, to be specific, how spectroscopic properties of pulp tissue could be utilized to detect infected mango. I hope that all attendees will enjoy and benefit from this webinar.
Date: 25 June 2025
Time: 12:00 pm CEST | 6:00 pm CST Asia | 7:00 pm JST
Webinar ID: 893 7340 0802
Webinar Secretariat: journal.webinar@mdpi.com
During the webinar, Prof. Dr. Chao-Hui Feng emphasized the need to broaden the applications of hyperspectral imaging and the potential application of terahertz spectroscopy to food. In addition, Professor Feng provided two examples of her own research, namely identifying naringin and hesperidin from waste orange peels by terahertz spectroscopy and evaluating the pH in sausages stuffed in modified casings with orange extracts by hyperspectral imaging. Afterwards, Mr. Fangchen Ding discussed the significant challenges that near-infrared spectroscopy faces in assessing the internal quality of packaged mangoes due to spectral interferences from packaging materials such as paper bags, PVC, PE, and EPE. To address this, Mr. Ding demonstrated how advanced strategies combining deep learning models, Gaussian spatial filtering, spectral preprocessing, and variable selection have been effective in mitigating interferences, concluding that these approaches significantly enhance the prediction accuracy of firmness, dry matter, soluble solids, and titratable acidity characteristics using near-infrared spectroscopy.
The webinar was hosted via Zoom and required registration to attend. The full recording can be found below. In order to learn about future webinars, you can sign up to our newsletter by clicking “Subscribe” at the top of the page.
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