Sorghum has a long history of cultivation and is an important food and economic crop. It can be divided into glutinous and non-glutinous varieties according to the starch structure and content. Rapid discrimination between the two would help the winemaking, feed, and food industries complete purchase pricing, ingredients, and quality control. In this study, 38 different samples were acquired, including 14 glutinous and 24 non-glutinous sorghum samples. Near infrared (NIR) spectra of glutinous and non-glutinous sorghum, pre-treated using the standard normal variable (SNV) transformation were found to have slightly different absorbances in the combination and first overtone bands. Based on the distribution of the starch-related and hydrogen-containing groups in the NIR region, it was concluded that glutinous sorghum has more C-O and C-C groups than non-glutinous sorghum. This study proposes an approach based on typical samples and direct calibration (TSDC) for binary discrimination. The TSDC approach consists of three functions. First, typical samples of two types of samples were selected. Second, typical type samples are used as dependent variables, predicted samples are used as independent variables, and formula regression is used to obtain fitted coefficients. Finally, if the formula regression model has no solution or the fitted coefficient is 1, typical type samples are reselected. Using the TSDC approach, discrimination accuracy can achieve 100% accuracy at 0.5 threshold. A larger threshold can be set to select better type characteristic predicted samples for discrimination. The TSDC approach can build excellent model through real relevance between the NIR spectra and the properties of interest, and the use of typical type samples greatly reduces modeling work compared with complex pattern recognition methods, especially for highly varied agricultural products. Therefore, it can efficiently propel the application and development of NIR detection technology. More research is required to apply the TSDC approach to three types of samples.
SantosPMSimeoneMLFPimentelMAG, et al.Non-destructive screening method for detecting the presence of insects in sorghum grains using near infrared spectroscopy and discriminant analysis. Microchem J2019; 149: 7. Article. DOI: 10.1016/j.microc.2019.104057.
2.
AwikaJMRooneyLW. Sorghum phytochemicals and their potential impact on human health. Phytochemistry2004; 65: 1199–1221. Review. DOI: 10.1016/j.phytochem.2004.04.001.
3.
ChenBRWangCYWangP, et al.Genome-wide association study for starch content and constitution in sorghum (Sorghum bicolor (L.) Moench). J Integr Agric2019; 18: 2446–2456. Article. DOI: 10.1016/s2095-3119(19)62631-6.
4.
PatersonAHBowersJEBruggmannR, et al.The Sorghum bicolor genome and the diversification of grasses. Nature2009; 457: 551–556. Article. DOI: 10.1038/nature07723.
5.
KaufmanRCWilsonJDBeanSR, et al.Sorghum starch properties as affected by growing season, hybrid, and kernel maturity. J Cereal Science2017; 74: 127–135. DOI: 10.1016/j.jcs.2017.01.014.
6.
PedersenJFBeanSRFunnellDL, et al.Rapid iodine staining techniques for identifying the waxy phenotype in sorghum grain and waxy genotype in sorghum pollen. Crop Science2004; 44: 764–767.
7.
ChenB-RWangC-yWangP, et al.Genome-wide association study for starch content and constitution in sorghum (Sorghum bicolor (L.) Moench). J Integr Agric2019; 18: 2446–2456. DOI: DOI: 10.1016/S2095-3119(19)62631-6
8.
ZhouLWangCGaoX, et al.Genome-wide variations analysis of sorghum cultivar Hongyingzi for brewing Moutai liquor. Hereditas2020; 157. DOI: 10.1186/s41065-020-00130-4.
9.
WuQCaoSXuY. Effects of glutinous and nonglutinous sorghums on Saccharomyces cerevisiae fermentation for Chinese liquor making. Int J Food Sci Technol2017; 52: 1348–1357. DOI: 10.1111/ijfs.13330.
10.
ZhuTJacksonDSWehlingRL, et al.Comparison of amylose determination methods and the development of a dual wavelength iodine binding technique. Cereal Chemistry2008; 85: 51–58. DOI: 10.1094/cchem-85-1-0051.
11.
MahmoodaTTurnerMAStoddardFL. Comparison of methods for colorinietric amylose determination in cereal grains. Starch-Starke2007; 59: 357–365. DOI: 10.1002/star.200700612.
12.
McclearyBVGibsonTSMugfordDC, et al.Measurement of total starch in cereal products by amyloglucosidase-α-amylase method: collaborative study. 1997; 80: 571–579.
13.
IwamotoRAmiyaSSaitoY, et al.FT-NIR spectroscopic study of OH groups in ethylene-vinyl alcohol copolymer. Applied Spectroscopy2001; 55: 864–870. DOI: 10.1366/0003702011952659.
14.
YanHLiP-HZhouG-S, et al.Rapid and practical qualitative and quantitative evaluation of non-fumigated ginger and sulfur-fumigated ginger via Fourier-transform infrared spectroscopy and chemometric methods. Food Chemistry2021; 341(pt 1): 128241. DOI: 10.1016/j.foodchem.2020.128241.
15.
PeirisKHSBeanSRChiluwalA, et al.Moisture effects on robustness of sorghum grain protein near-infrared spectroscopy calibration. Cereal Chemistry2019; 96: 678–688. DOI: 10.1002/cche.10164.
16.
DantasHVBarbosaMFNascimentoECL, et al.An automatic flow system for NIR screening analysis of liquefied petroleum gas with respect to propane content. Talanta2013; 106: 158–162. DOI: 10.1016/j.talanta.2012.12.024.
17.
AnthonyCMSulafARobertAW. Identifying counterfeit medicines using near infrared spectroscopy. J Near Infrared Spectroscopy2010; 18: 1–15. DOI: 10.1255/jnirs.856.
18.
TonyWGerardDColmPOD. Better quality food and beverages: the role of near infrared spectroscopy. J Near Infrared Spectroscopy2008; 16: 1–29. DOI: 10.1255/jnirs.758.
19.
HossainMZGotoT. Near- and mid-infrared spectroscopy as efficient tools for detection of fungal and mycotoxin contamination in agricultural commodities. World Mycotoxin J2014; 7: 507–515. DOI: 10.3920/wmj2013.1679.
20.
LiJDanaoM-GCChenS-F, et al.Prediction of starch content and ethanol yields of sorghum grain using near infrared spectroscopy. J Near Infrared Spectroscopy2015; 23: 85–92. DOI: 10.1255/jnirs.1146.
21.
DykesLHoffmannLJr.Portillo-RodriguezO, et al.Prediction of total phenols, condensed tannins, and 3-deoxyanthocyanidins in sorghum grain using near-infrared (NIR) spectroscopy. Journal of Cereal Science2014; 60: 138–142. DOI: 10.1016/j.jcs.2014.02.002.
22.
QinHWangH-RLiW-J, et al.Application of DPLS-based LDA in corn qualitative near infrared spectroscopy analysis. Spectroscopy and Spectral Analysis2011; 31: 1777–1781. DOI: 10.3964/j.issn.1000-0593(2011)07-1777-05.
23.
ZontovYVRodionovaOYKucheryavskiySV, et al.DD-SIMCA—A MATLAB GUI tool for data driven SIMCA approach. Chemometrics and Intelligent Laboratory Systems2017; 167: 23–28. DOI: 10.1016/j.chemolab.2017.05.010.
24.
Perez-MarinDGarrido-VaroAGuerreroJE, et al.Use of artificial neural networks in near-infrared reflectance spectroscopy calibrations for predicting the inclusion percentages of wheat and sunflower meal in compound feedingstuffs. Applied Spectroscopy2006; 60: 1062–1069. DOI: 10.1366/000370206778397506.
25.
YangXWYuQZHeLF, et al. The one-against-all partition based binary tree support vector machine algorithms for multi-class classification. Neurocomputing2013; 113: 1–7. DOI: 10.1016/j.neucom.2012.12.048.
26.
DavariSAMukherjeeDDeep learning models for data-driven laser induced breakdown spectroscopy (LIBS) analysis of interstitial oxygen impurities in Czochralski-Si crystals. Applied Spectroscopy2022; 76: 667–677. DOI: 10.1177/00037028221085640.
27.
LiuY-lZhangH-yPengH-g, et al.Reliability and chemical composition analysis of tobacco leaf grade model by near-infrared spectroscopy. Spectroscopy Spectral Analysis2020; 40: 3260–3264. DOI: 10.3964/j.issn.1000-0593(2020)10-3260-05.
28.
LuanLWangYLiX, et al.Application of multiple classifier fusion in the discriminant analysis of near infrared spectroscopy for agricultural products. J Near Infrared Spectroscopy2016; 24: 363–372. DOI: 10.1255/jnirs.1236.
29.
SchulzeHGFoistRBIvanovA, et al. Fully automated high-performance signal-to-noise ratio enhancement based on an iterative three-point zero-order savitzky-golay filter. Applied Spectroscopy2008; 62: 1160–1166. DOI: 10.1366/000370208786049079.
30.
FeifeiTHaiboYZuzanaH, et al.Detection of aflatoxin B1 on corn kernel surfaces using visible-near infrared spectroscopy. J Near Infrared Spectroscopy2020; 28: 59–69. DOI: 10.1177/0967033519895686.
31.
BiYYuanKXiaoW, et al.A local pre-processing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation. Analytica Chimica Acta2016; 909: 30–40. DOI: 10.1016/j.aca.2016.01.010.
32.
WangY-hHuW-ySongP-f, et al.Model transfer between different fourier instruments and the analysis of error. Spectroscopy Spectral Analysis2019; 39: 964–968. DOI: 10.3964/j.issn.1000-0593(2019)03-0964-05.
33.
GenkawaTShinzawaHKatoH, et al.Baseline correction of diffuse reflection near-infrared spectra using searching region standard normal variate (SRSNV). Applied Spectroscopy2015; 69: 1432–1441. DOI: 10.1366/15-07905.
34.
FaberKLorberAKowalskiBR. Analytical figures of merit for tensorial calibration. J Chemometrics1997; 11: 419–461.
35.
BookshKSKowalskiBR. Calibration method choice by comparison of model basis functions to the theoretical instrumental response function. Analytica Chimica Acta1997; 348: 1–9. DOI: 10.1016/s0003-2670(96)00604-6.
36.
LorberAFaberKKowalskiBR. Net analyte signal calculation in multivariate calibration. Anal Chem1997; 69: 1620–1626. Article. DOI: 10.1021/ac960862b.
37.
GeladiPMartensH. A calibration tutorial for spectral data. part 1: data pretreatment and principal component regression using matlab. J Near Infrared Spectroscopy1996; 4: 225–242. DOI: 10.1255/jnirs.93.
38.
HellandISSaeboSAlmoyT, et al.Model and estimators for partial least squares regression. J Chemometrics2018; 32. Article. DOI: 10.1002/cem.3044.
39.
LiX-yShuR-xLuanL-l, et al.Study on the analysis of the proportion of mixed samples with near infrared spectroscopy and non-negative coefficients regression. Spectroscopy Spectral Analysis2016; 36: 967–971. DOI: 10.3964/j.issn.1000-0593(2016)04-0967-05.
40.
OsborneB GFearnT. Near-Infrared Spectroscopy in Food Analysis. New York, NY: Longman Scientific and Technical, 1986.
41.
YanY LZhaoL LHanD H, et al.Foundation and Application of NIR Spectra Analysis. Beijing, China: China Light Industry Press, 2005.
42.
ChuXLYuanHFLuWZ. Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique. Prog Chem2004; 16: 528–542. Review
43.
StarkELuchterK. Near-infrared analysis (NIRA) - A technology for quantitative and qualitative-analysis. Applied Spectroscopy reviews1986; 22: 335–399. Review. DOI: 10.1080/05704928608060440.
44.
LiuYLHimmelsbachDSBartonFE. Two-dimensional fourier transform Raman correlation spectroscopy determination of the glycosidic linkages in amylose and amylopectin. Applied spectroscopy2004; 58: 745–749. DOI: 10.1366/000370204873006.
45.
TesterRFKarkalasJQiX. Starch - composition, fine structure and architecture. J Cereal Science2004; 39: 151–165. DOI: 10.1016/j.jcs.2003.12.001.
46.
Sha-shaLIH-pLBOMZ. Advances in characterization of amylose and amylopectin starch. Food Science2010; 31: 273–277.
47.
LiuQYutingQIFanglingDU, et al.Research and development of sorghum starch. Farm Products Process2016; 18: 54–56. DOI: 10.16693/j.cnki.1671-9646(X).2016.09.045.
