Chromium (Cr) is a toxic heavy metal that is harmful to human beings if it enters the foodchain. The traditional analytical method to measure Cr content in samples is complex and expensive. Therefore, it is desirable to establish a rapid and cost-effective method for the determination of Cr content. In this study, used near infrared spectroscopy combined with chemometrics was to measure the Cr content in cottonseed meal. Multiplicative scatter correction was selected as the optimal spectral pre-treatment. Monte Carlo uninformative variable elimination (MC-UVE) was applied to select informative wavelengths from the full spectrum. Finally, the optimal near infrared calibration model for Cr determination in cottonseed meal was generated with high coefficients of correlation (R2 of 0.93) and ratio performance deviation (RPD) of 3.5. These results indicated that the model is efficient and accurate for rapid measurement of Cr content in cottonseed meal, which can substitute the traditional analytical method.
KhanMAWahidAAhmadM, et al.World cotton production and consumption: an overview. In: AhmadSHasanuzzamanM (eds) Cotton Production and Uses. Singapore: Springer, 2020, pp. 1–7.
2.
LiuHYQuampahAChenJH, et al.QTL analysis for gossypol and protein contents in upland cottonseeds with two different genetic systems across environments. Euphytica2012; 188: 453–463.
3.
QuampahAHuangZRWuJG, et al.Estimation of oil content and fatty acid composition in cottonseed kernel powder using near infrared reflectance spectroscopy. J Am Oil Chem Soc2012; 89: 567–575.
4.
SawanZMHafezSABasyonyAE, et al.Cottonseed, protein, oil yields and oil properties as affected by nitrogen fertilization and foliar application of potassium and a plant growth retardant. World J Agric Sci2006; 2: 56–65.
5.
JohnsonJSchewelLGraedelT. The contemporary anthropogenic chromium cycle. Environ Sci Technol2006; 40: 7060–7069.
6.
The Ministry of Environmental Protection (MEP). The Ministry of Land and Resources Report on the National Soil Contamination Survey;. Ministry of Environmental Protection of the People’s Republic of China, 2014.
7.
WatanabeTAzumaT. Ionomic variation in leaves of 819 plant species growing in the botanical garden of Hokkaido University, Japan. J Plant Res2021; 134: 291–304.
8.
DaudMKMeiLVariathMT, et al.Chromium (VI) uptake and tolerance potential in cotton cultivars: effect on their root physiology, ultramorphology, and oxidative metabolism. BioMed Res Int2014; 2014: 1–12.
9.
PasquiniC. Near infrared spectroscopy: a mature analytical technique with new perspectives-A review. Anal Chim Acta2018; 1026: 8–36.
10.
ZhangXFYuXYanJX, et al.Measurement of moisture and oil content in gross cottonseed based on near infrared reflectance technique by open detecting mode. Spectrosc Spectr Anal2007; 27: 473–476.
11.
HuangZRShaSRongZQ, et al.Rapid and nondestructive determination of fatty acid contents in shell-intact cottonseed by near infrared spectroscopy. Chin J Anal Chem2013; 41: 922–926.
12.
HuangZRChenJHLiuHY, et al.Determination of amino acid contents in cottonseeds using near infrared reflectance spectroscopy. Spectrosc Spectr Anal2011; 31: 2692–2696.
13.
LiCZhaoTLLiC, et al.Determination of gossypol content in cottonseeds by near infrared spectroscopy based on Monte Carlo uninformative variable elimination and nonlinear calibration methods. Food Chem2017; 221: 990–996.
14.
ZhaoRXuXLiJ, et al.Rapid determination of phytic acid content in cottonseed meal via near infrared spectroscopy. J Near Infrared Spectrosc2017; 25: 188–195.
15.
ZhangHHuXYLiuLM, et al.Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil. Spectrochim Acta Mol Biomol Spectrosc2022; 270: 120841.
16.
ZhuXLiGShanY. Prediction of cadmium content in brown rice using near-infrared spectroscopy and regression modelling techniques. Int J Food Sci Technol2015; 50: 1123–1129.
17.
MorosJLlorcaICerveraML, et al.Chemometric determination of arsenic and lead in untreated powdered red paprika by diffuse reflectance near-infrared spectroscopy. Anal Chim Acta2008; 613: 196–206.
18.
XiaXQMaoYQJiJF, et al.Reflectance spectroscopy study of Cd contamination in the sediments of the Changjiang River, China. Environ Sci Technol2007; 41: 3449–3454.
19.
NingYLiJHCaiWS, et al.Simultaneous determination of heavy metal ions in water using near-infrared spectroscopy with preconcentration by nano-hydroxyapatite. Spectrochim Acta Mol Biomol Spectrosc2012; 96: 289–294.
20.
KumagaiMOhisaNAmanoT, et al.Canonical discriminant analysis of cadmium content levels in unpolished rice using a portable near-infrared spectrometer. Anal Sci2003; 19: 1553–1555.
21.
FontRVélezDRío-CelestinoMD, et al.Screening inorganic arsenic in rice by visible and near-infrared spectroscopy. Microchim Acta2005; 151: 231–239.
22.
XuDYChenSCViscarra RosselRA, et al.X-ray fluorescence and visible near infrared sensor fusion for predicting soil chromium content. Geoderma2019; 352: 61–69.
23.
RinnanABergFEngelsenSB. Review of the most common pre-processing techniques for near-infrared spectra. TrAC, Trends Anal Chem2009; 28: 1201–1222.
24.
CaiWSLiYKShaoXG. A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemomtr Intell Lab2008; 90: 188–194.
25.
Esteban-DíezIGonzález-SáizJMSáenz-GonzálezC, et al.Coffee varietal differentiation based on near infrared spectroscopy. Talanta2007; 71: 221–229.
26.
KennardRWStoneLA. Computer aided design of experiments. Technometrics1969; 11: 137–148.
27.
YehTFChangHMKadlaJF. Rapid prediction of solid wood lignin content using transmittance near-infrared spectroscopy. J Agric Food Chem2004; 52: 1435–1439.
28.
SuykensJAKDe BrabanterJLukasL, et al.Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing2002; 48: 85–105.
29.
ÜstünBMelssenWJOudenhuijzenM, et al.Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization. Anal Chim Acta2005; 544: 292–305.
30.
RosalesAGaliciaLOviedoE, et al.Near-infrared reflectance spectroscopy (NIRS) for protein, tryptophan, and lysine evaluation in quality protein maize (QPM) breeding programs. J Agric Food Chem2011; 59: 10781–10786.
31.
AranaIJarénCArazuriS. Maturity, variety and origin determination in white grapes (Vitis Vinifera L.) using near infrared reflectance technology. J Near Infrared Spectrosc2005; 13: 349–357.
32.
MalleyDFMartinPDBen-DorE. Application in analysis of soil. In: RobertsCAWorkmanJJReevesJB (eds) Near-infrared spectroscopy in agriculture, agronomy monograph. Madison, WI: ASA, CSSA and SSSA, 2004, vol 44, pp. 729–784.
33.
WilliamsPNorrisK. Near-infrared technology: in the agricultural and food industries. 2nd ed.St. Paul, MN: Cereals & Grains Association, 2001.
34.
WuYZhangXLiaoQ, et al.Can contaminant elements in soils be assessed by remote sensing technology. Soil Sci2011; 176: 196–205.
35.
HourantPBaetenVMoralesMT, et al.Oil and fat classification by selected bands of near-infrared spectroscopy. Appl Spectrosc2000; 54: 1168–1174.
36.
SatoTKawanoSIwamotoM. Near infrared spectral patterns of fatty acid analysis from fats and oils. J Am Oil Chem Soc1991; 68: 827–833.
37.
MoshtaghieAAAniMBazrafshanMR. Comparative binding study of aluminum and chromium to human transferrin. Effect of iron. Biol Trace Elem Res1992; 32: 39–46.
38.
PlaperAJenko-BrinovecSPremzlA, et al.Genotoxicity of trivalent chromium in bacterial cells. Possible effects on DNA topology. Chem Res Toxicol2002; 15: 943–949.
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