Analysis of secondary biochemical components in maize flour samples by NIR (near infrared reflectance) spectroscopy

KAHRIMAN F., Onaç I., Oner F., MERT F., EGESEL C. Ö.

JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION, cilt.14, sa.4, ss.2320-2332, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 4
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s11694-020-00479-0
  • Dergi Adı: JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, CAB Abstracts, Compendex, Food Science & Technology Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.2320-2332
  • Anahtar Kelimeler: Zea mays, Minor quality traits, Wavelength, Spectrum, PHYTIC ACID, RAPID-DETERMINATION, AMYLOSE CONTENT, PROTEIN, PREDICTION, QUALITY, STARCH, CORN, OIL, CALIBRATION
  • Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet

Özet

This study was carried out to determine whether it is possible to detect secondary biochemical components in maize flour samples by near infrared reflectance (NIR) spectroscopy. Two hundred fifty maize samples were used as the material. Calibration models were developed for six different secondary biochemical components, namely amylose, amylopectin, lysine, tryptophan, zein, and phytic acid. The robustness of the calibration models (n = 200) was tested by external validation (n = 50). Results showed that NIR spectroscopy could be used to detect secondary quality components in maize. The most successful prediction model was for amylose content (SEP: 1.784%, RPD: 3.09, r = 0.963). Models for the other traits (amylopectin, zein, lysine, tryptophan, phytic acid) gave acceptable results (RPD > 2) for material screening purposes. Target traits subjected to calibration studies were found to be related to the different overtone regions of C-H, N-H and S-H bond vibrations in scanning the spectral region. It seems that it is necessary to improve the prediction performance of the models using different approaches, such as broadening the spectral area and/or using chemometric technique combinations.