Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy

• Published in: Applied sciences 2023-04, Vol.13 (8), p.5143
• Main Authors: Johnson, Joel B, El Orche, Aimen, Mani, Janice S, Aït-Kaddour, Abderrahmane, Walsh, Kerry B, Naiker, Mani
• Format: Article
• Language: English
• Subjects: antioxidant; Antioxidants; Capsicum annuum; Chemical Sciences; Dry matter; FTIR spectroscopy; Infrared spectra; Infrared spectroscopy; Laboratories; Least squares method; Near infrared radiation; near-infrared (NIR) spectroscopy; NIRS; Optimization; Peppers; Phenolic compounds; phenolics; Phenols; Spectroscopy; Spectrum analysis; Support vector machines; Variables; Vitamin A; Vitamin C
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13085143

The aim of the present study was to evaluate the potential of handheld near-infrared (NIR) and benchtop mid-infrared (MIR) spectroscopy for the rapid prediction of antioxidant capacity, dry matter, and total phenolic contents in cayenne pepper (Capsicum annuum ‘Cayenne’). Using NIR spectroscopy, the best-performing model for dry matter had an R2pred = 0.74, RMSEP = 0.38%, and RPD of 2.02, exceeding the best results previously reported in the literature. This was also the first study to predict dry matter content from the mid-infrared spectra, although with lower accuracy (R2pred = 0.54; RMSEP = 0.51%, RPD 1.51). The models for antioxidant capacity and total phenolic content did not perform well using NIR or MIR spectroscopy (RPD values < 1.5), indicating that further optimization is required in this area. Application of support vector regression (SVR) generally gave poorer results compared to partial least squares regression (PLSR). NIR spectroscopy may be useful for in-field measurement of dry matter in the chili crop as a proxy measure for fruit maturity. However, the lower accuracy of MIR spectroscopy is likely to limit its use in this crop.