Abstract
A pilot study was conducted to develop nondestructive calibration models to discriminate organic and conventional rice from selected field trials in Heilongjiang Province, China using near-infrared (NIR) spectroscopy with the absorption mode in the wave number range of 12000–4000 cm−1. Multivariate methods such as principal component analysis (PCA) and partial least squares (PLS) regression were used to interpret the NIR spectral data. PLS regression was used for discrimination between organic and conventional rice samples after several pretreatments of the spectra. The coefficient of determination (R2) value for the PLS regression model was 0.8430 with a standard error for cross validation (SECV) of 0.1992 and a root mean square error for cross validation (RMSECV) of 0.1982. Overall, the results indicated good performance of the prediction models and supported the capability of NIR spectroscopy to classify between discriminate organic and conventional rice. This study further supports the utilization of NIR in the discriminative analysis of foods and as a noteworthy method for the authentication of organic rice at the industrial level.
Graphical abstract
Similar content being viewed by others
References
K. Matsumura et al., Mapping the global supply and demand structure of rice. Sustain. Sci. 4(2), 301–313 (2009)
M. Huber et al., Organic food and impact on human health: assessing the status quo and prospects of research. NJAS - Wageningen J. Life Sci. 58(3), 103–109 (2011)
Guidelines for the production, processing, labelling andmarketing of organically produced foods., C.A. Commission and FAO, Editors. 1999: 1999
V. Worthington, Nutritional quality of organic versus conventional fruits, vegetables, and grains. J. Altern. Complement. Med. 7(2), 161–173 (2001)
P. Flores et al., Classification of organic and conventional sweet peppers and lettuce using a combination of isotopic and bio-markers with multivariate analysis. J. Food Compos. Anal. 31(2), 217–225 (2013)
F.J. Cuevas et al., Effect of management (organic vs conventional) on volatile profiles of six plum cultivars (Prunus salicina Lindl.). A chemometric approach for varietal classification and determination of potential markers. Food Chem. 199, 479–484 (2016)
Y. Suzuki et al., Multiple stable isotope analyses for verifying geographical origin and agricultural practice of japanese rice samples. Bunseki Kagaku 58(58), 1053–1058 (2009)
S.D. Kelly, A.S. Bateman, Comparison of mineral concentrations in commercially grown organic and conventional crops—Tomatoes (Lycopersicon esculentum) and lettuces (Lactuca sativa). Food Chem. 119(2), 738–745 (2010)
P. Mäder et al., Wheat quality in organic and conventional farming: results of a 21 year field experiment. J. Sci. Food Agric. 87(10), 1826–1835 (2007)
A. Vlachos, I.S. Arvanitoyannis, A review of rice authenticity/adulteration methods and results. Crit. Rev. Food Sci. Nutr. 48(6), 553–598 (2008)
E.M. Borges et al., Monitoring the authenticity of organic rice via chemometric analysis of elemental data. Food Res. Int. 77, 299–309 (2015)
A.M.C. Davies, William Herschel and the discovery of near infrared. J. Near Infrared Spectrosc. 11(1), 3–5 (2000)
K.H. Norris, Design and development of a new moisture meter. Agric. Eng. 45, 370 (1964)
L. Salguero-Chaparro et al., Feasibility of using NIR spectroscopy to detect herbicide residues in intact olives. Food Control 30(2), 504–509 (2013)
M.V. Reboucas et al., Near-infrared spectroscopic prediction of chemical composition of a series of petrochemical process streams for aromatics production. Vib. Spectrosc. 52(1), 97–102 (2010)
J. Luypaert, D.L. Massart, Y. Vander, Heyden, Near-infrared spectroscopy applications in pharmaceutical analysis. Talanta 72(3), 865–883 (2007)
Q. Chen, J. Zhao, H. Lin, Study on discrimination of roast green tea (Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition. Spectrochim Acta A Mol Biomol. Spectrosc. 72(4), 845–850 (2009)
Y. Ni, M. Mei, S. Kokot, Analysis of complex, processed substances with the use of NIR spectroscopy and chemometrics: classification and prediction of properties—the potato crisps example. Chemometr. Intell. Lab. Syst. 105(2), 147–156 (2011)
P.D. Alamar et al., Quality evaluation of frozen guava and yellow passion fruit pulps by NIR spectroscopy and chemometrics. Food Res. Int. 85, 209–214 (2016)
S. Munera et al., Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. LWT—Food Sci. Technol. 77, 241–248 (2017)
X. Li, Y. He, C. Wu, Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy. J. Stored Prod. Res. 44(3), 264–268 (2008)
A. Heman, C.L. Hsieh, Measurement of moisture content for rough rice by visible and near-infrared (NIR) spectroscopy. Eng. Agric. Environ. Food 9(3), 280–290 (2016)
L.H. Xie et al., Optimisation of near-infrared reflectance model in measuring protein and amylose content of rice flour. Food Chem. 142(2), 92–100 (2014)
R.R. Gangidi, A. Proctor, J.-F. Meullenet, Milled rice surface lipid measurement by diffuse reflectance fourier transform infrared spectroscopy (DRIFTS). J. Am. Oil Chem. Soc. 79(1), 7–12 (2002)
V. Loewe et al., Discriminant analysis of Mediterranean pine nuts (Pinus pinea L.) from Chilean plantations by near infrared spectroscopy (NIRS). Food Control, 73, 634–643 (2016)
H. Ayvaz et al., The use of infrared spectrometers to predict quality parameters of cornmeal (corn grits) and differentiate between organic and conventional practices. J. Cereal Sci. 62, 22–30 (2015)
S. Serranti et al., Classification of oat and groat kernels using NIR hyperspectral imaging. Talanta 103, 276–284 (2013)
E.T. Champagne et al., Correlation between cooked rice texture and rapid visco analyser measurements. Cereal Chem. 76(5), 764–771 (1999)
S. Wold, Principle componant analysis. Chemom. Intell. Lab. Syst. 2, 37–52 (1987)
S. Roussel et al., Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry. J. Food Eng. 60(4), 407–419 (2003)
B.M. Nicolaï et al., Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol. Technol. 46(2), 99–118 (2007)
M. Plans, J. SimÓ, F. Casañas, et al., Characterization of common beans (Phaseolus vulgaris L.) by infrared spectroscopy: comparison of MIR, FT-NIR and dispersive NIR using portable and benchtop instruments. Food Res. Int. 54(2), 1643–1651 (2013)
L.M. Dale et al., Discrimination of grassland species and their classification in botanical families by laboratory scale NIR hyperspectral imaging: preliminary results. Talanta 116, 149–154 (2013)
P. Geladi, E. Dåbakk, An overview of chemometrics applications in near infrared spectrometry. J. Near Infrared Spectrosc. 3(1), 119 (1995)
Y. Chen et al., Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques. Anal. Chim. Acta 618(2), 121–130 (2008)
L. Moseholm, Analysis of air pollution plant exposure data: the soft independent modelling of class analogy (SIMCA) and partial least squares modelling with latent variable (PLS) approaches. Environ. Pollut. 53(1–4), 313 (1988)
R.G. Brereton, Introduction to multivariate calibration in analytical chemistry. Analyst 125(11), 2125–2154 (2000)
P.F. Velleman, R.E. Welsch, Efficient computing of regression diagnostics. Am. Stat. 35(4), 234–242 (1981)
W. Saeys, A.M. Mouazen, H. Ramon, Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosys. Eng. 91(4), 393–402 (2005)
H. Jiang et al., Identification of solid state fermentation degree with FT-NIR spectroscopy: comparison of wavelength variable selection methods of CARS and SCARS. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 149, 1–7 (2015)
S.L. Cantor et al., NIR Spectroscopy applications in the development of a compacted multiparticulate system for modified release. AAPS PharmSciTech 12(1), 262–278 (2011)
F. Westad, F. Marini, Validation of chemometric models—a tutorial. Anal. Chim. Acta 893, 14 (2015)
D. Kusumaningrum et al., Nondestructive technique for determining the viability of soybean (glycine max) seeds using FT-NIR spectroscopy. J. Sci. Food Agric. 98(5), 1734–1742 (2018)
G. Downey, J.D. Kelly, Detection and quantification of apple adulteration in strawberry and raspberry purees using visible and near infrared spectroscopy. J. Agric. Food Chem. 52, 204–209 (2004)
S.R. Delwiche et al., Apparent amylose content of milled rice by near-infrared reflectance spectrophotometry. Cereal Chem. 72(2), 182–187 (1995)
Acknowledgements
The authors would like to acknowledge the financial support provided by the Research & Development Projects of Heilongjiang Province (Project GA14B104). We would also like to thank the Heilongjiang Academy of Agricultural Sciences for providing rice grain information and McGill University and Northeast Agricultural University for scientific support and technical advice.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Xiao, R., Liu, L., Zhang, D. et al. Discrimination of organic and conventional rice by chemometric analysis of NIR spectra: a pilot study. Food Measure 13, 238–249 (2019). https://doi.org/10.1007/s11694-018-9937-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11694-018-9937-7