Milling quality assessment of Khao Dok Mali 105 milled rice by near-infrared reflectance spectroscopy technique

Abstract

The objective of this study was to assess milling quality of Khao Dok Mali 105 milled rice. The physicochemical properties of milled rice can be analyzed in values of milling quality as degree of milling (DOM), surface lipid content (SLC), color as L*, a*and b*, and the whiteness index using various kinds of measuring instrument. However in this study, the calibration models were developed using only the near infrared reflectance spectroscopy (NIRS) to determine the various physicochemical properties at wavelengths between 1,100 and 2,500 nm. The signal pretreatment and partial least square (PLS) regression were used to validate the models for the measurement of DOM, SLC, L*, a*, b* and whiteness index. Six calibration models of these properties were optimized based on the cross validation correlations (Rcv), standard error of cross validation (SECV), the external validation correlations (Rv) and standard error of prediction (SEP). It was found that the Rcv for these six models were 0.98, 0.99, 0.98, 0.95, 0.85 and 0.98 while the Rv for those were 0.99, 0.99, 0.98, 0.96, 0.88 and 0.99, the correlation were closed to 1 which shown that the agreement between data and the models were satisfied. In the meantime, the SECV and the SEP of these six calibration models shew less fluctuation of data and models. As the results, the calibration models developed in this study can be used to predict the milling quality of KDML 105 milled rice. The relationships between DOM and the five parameters were also investigated in this study.

Rapid Determination of the Oil and Moisture Contents in Camellia gauchowensis Chang and Camellia semiserrata Chi Seeds Kernels by Near-infrared Reflectance Spectroscopy

“…Based on the resolution of the analytical and quality factors from food samples with correlation of electromagnetic absorption at aforementioned wavelength, NIRS is used routinely in sensory, physical and chemical analysis of food and agricultural products [ 17 , 18 , 19 ]. As reported, it has been widely used in different crops, such as sesame [ 20 ], maize [ 21 ], rice [ 22 ], soybean [ 23 ] and sunflower [ 24 ], to predict oil, moisture, protein, fatty acids, phenols or crude fiber.…”

Prediction of fatty acid composition of sunflower seeds by near-infrared reflectance spectroscopy

“…This technology is based on the measurement of absorption of electromagnetic radiation in 400-2500 nm wavelength range (Davies and Granth 1987). NIR spectroscopy, based on the resolution of the analytical and quality factors from food samples with correlation of electromagnetic absorption at aforementioned wavelength, is used routinely in sensory, physical and chemical analysis of food and agricultural products (Williams 2001;Izneid et al 2014;Madalozzo et al 2015;Srikham and Noomhorm 2015). For this purpose, studies were conducted to determine crude fat content of the oil plants and their fatty acid compositions by NIR spectroscopy (Fassio and Cozzolino 2004;Velasco et al 2004;Biskupek-Korell and Moschner 2006;Koprna et al 2006).…”

Rapid prediction of Apparent Amylose, total starch, and crude protein by near‐infrared reflectance spectroscopy for foxtail millet (Setaria italica)

“…The common spectral preprocessing methods include smoothing, derivation, multiple scatter correction (MSC; Karstang & Kvalheim, 1991), SNV (Helland, Næs, & Isaksson, 1995), wavelet transform (WT; Estienne, Massart, Zanier‐Szydlowski, & Marteau, 2000), and constant offset elimination (OSC; Wold, Antti, Lindgren, & Ohman, 1998). This study used three mathematical methods to optimize spectral data, among which the method of smoothing eliminates noise, the method of derivation reduces the baseline drift and reveals the information hidden in the spectra, and the method of SNV eliminates multispectral deviation (Srikham & Noomhorm, 2015). By optimizing these three methods, and combined with the best regression method PLS, a near‐infrared prediction model of the main components of foxtail millet grain and dehusked millet grain was established.…”

“…Regarding agriculture, various NIRS models for the evaluation of crop quality have been established for several crops, including rice (Srikham & Noomhorm, 2015), corn (Park et al, 2015), wheat (Baslar & Ertugay, 2011), and rape (Dimov, Suprianto, Hermann, & Mollers, 2012). Many components such as protein, total starch, crude fat, and carbohydrates have been successfully estimated (Haase, 2003; Koprna, Nerusil, Kolovrat, Kucera, & Kohoutek, 2006; Rosales, Galicia, Oviedo, Islas, & Palacios‐Rojas, 2011).…”