Development of Models for the Prediction of Domestic Red Pepper (Capsicum annuum L.) Powder Capsaicinoid Content using Visible and Near-infrared Spectroscopy

Abstract

Purpose:The purpose of this study was to non-destructively and quickly predict the capsaicinoid content of domestic red pepper powders from various areas of Korea using a pungency measurement system in combination with visible and near-infrared (VNIR) spectroscopic techniques. Methods: The reflectance spectra of 149 red pepper powder samples from 14 areas of Korea were obtained in the wavelength range of 450-950 nm and partial least squares regression (PLSR) models for the prediction of capsaicinoid content were developed using area models. Results: The determination coefficient of validation (RV2), standard error of prediction (SEP), and residual prediction deviation (RPD) for the capsaicinoid content prediction model for the Namyoungyang area were 0.985, ±2.17 mg/100g, and 7.94, respectively. Conclusions: These results show the possibility of VNIR spectroscopy combined with PLSR models in the non-destructive and facile prediction of capsaicinoid content of red pepper powders from Korea.

Untargeted classification for paprika powder authentication using visible – Near infrared spectroscopy (VIS-NIRS)

“…In the case of paprika or related products, NIRS has been mainly used for quantification. For example, to quantify ASTA color, moisture (Bae, Han, & Hong, 1998), capsaicinoids (Lim, Kim, Mo, & Kim, 2015;Park et al, 2008), arsenic and lead (Moros et al, 2008), soluble solids content (SSC), firmness of peppers (Penchaiya, Bobelyn, Verlinden, Nicolaï, & Saeys, 2009) and mycotoxins (Hernández-Hierro, García-Villanova, & González-Martín, 2008). In addition, Vis-NIRS combined with multivariate analysis has been used to determine total carotenoids, chlorophylls, as well as maturity stage of intact peppers (Timea Ignat et al, 2013) and ascorbic acid (T. Ignat, Schmilovitch, Fefoldi, Steiner, & Alkalai-Tuvia, 2012).…”

Fluorescence hyperspectral imaging technique for foreign substance detection on fresh‐cut lettuce

“…Some recent studies have focused on detecting foreign materials in agricultural products and food using camera vision techniques, near infrared spectroscopy, fluorescence spectroscopy, and hyperspectral imaging (HSI) techniques . Camera vision techniques based on image processing measure the colour, shape and other quality attributes of agricultural products and foods.…”

Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging

“…Various studies aiming to determine external and internal quality and analyze the defects of agricultural products such as vegetables and fruits have been performed using near-infrared (NIR) spectroscopy, machine vision, multispectral imaging, and hyperspectral imaging techniques [ 9 , 10 , 11 , 12 , 13 ]. The conventional image system is incapable in inspecting specimens with similar color and discriminate complex objectives [ 14 ].…”