Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques

• Main Authors: Amanah, Hanim Z., Tunny, Salma Sultana, Masithoh, Rudiati Evi, Choung, Myoung-Gun, Kim, Kyung-Hwan, Kim, Moon S., Baek, Insuck, Lee, Wang-Hee, Cho, Byoung-Kwan
• Format: Article PeerReviewed
• Language: English
• Published: MDPI 2022
• Subjects: Biological Sciences not elsewhere classified; Horticultural Crop Growth and Development; Horticultural Crop Improvement (Selection and Breeding); Food technology
• Online Access: https://repository.ugm.ac.id/284571/1/foods-11-00232-v2.pdf; https://repository.ugm.ac.id/284571/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123548582&doi=10.3390%2ffoods11020232&partnerID=40&md5=8a864b2a87e6cd487c248cdce76f6c19
• Institution: Universitas Gadjah Mada

The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R2p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R2p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R2p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R2 and error to the calibration model.