Abstract
Peanut (Arachis hypogaea L.) is rich in some important oils such as the high content of polyunsaturated fatty acids. The potential of hyperspectral imaging technique in the spectral range I (400–1000 nm) and II (1000–2500 nm) coupled with chemometrics analysis for predicting oil content in different peanut cultivars was investigated in this study. Hyperspectral images were obtained and the corresponding spectral data was extracted. Quantitative calibration models were established between pre-processing spectral data and the reference measured oil content by partial least squares regression (PLSR) analysis. By comparing the model performances based on different spectral pre-processing methods, the raw-PLSR models using full wavelengths presented better results with the determination coefficient (R2 p) of 0.696 and 0.923, and root mean square errors by prediction (RMSEP) of 0.416 % and 0.208 %, respectively. In addition, six optimal wavelengths in the spectral range II were selected based on the regression coefficients of the established raw-PLSR model. The simplified PLSR model established only using identified optimal wavelengths also showed good performance with R2 p of 0.934, and RMSEP of 0.197 %. The results demonstrated that hyperspectral imaging technique is a promising tool for rapid and non-destructive determination of oil content in peanut.
Similar content being viewed by others
References
Abdi H (2010) Partial least squares regression and projection on latent structure regression (PLS regression). Wiley interdisciplinary reviews. Comput Stat 2:97–106
Amigo JM, Babamoradi H, Elcoroaristizabal S (2015) Hyperspectral image analysis. A tutorial. Anal Chim Acta 896(10):34–51
Bauriegel E, Giebel A, Geyer M, Schmidt U, Herppich WB (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput Electron Agric 75:304–312
Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37:911–917
Brown DF, Cater CM, Mattil KF et al (1975) Effect of variety, growing location and their interaction on the fatty acid composition of peanuts. J Food Sci 40(5):1055–1060
Cheng JH, Dai Q, Sun DW, Zeng XA, Liu D, Pu HB (2013) Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends Food Sci Technol 34(1):18–31
ElMasry G, Sun DW, Allen P (2013) Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. J Food Eng 117:235–246
Firestone D (1993) Official methods and recommended practices of the American oil chemists’ society, vol 4th. AOCS Press, Champaign
Folch J, Lees M, Sloane Stanley GH (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226:497–509
He HJ, Wu D, Sun DW (2013) Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innovative Food Sci Emerg Technol 18:237–245
Holley KT, Hammons RO (1968) Strain and seasonal effects on peanut characteristics. Res. Bull. 32 Ga agric. Exp. Stn 27
Holman RT, Edmondson PR (1956) Near-infrared spectra of fatty acids and some related substances. Anal Chem 28(10):1533–1538
Hurburgh CR, Rippke GR, Cogdill RP (2004) Single-kernel maize analysis by near-infrared hyperspectral imaging. Trans Asae 47(1):311–320
Iqbal A, Sun DW, Allen P (2013) Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system. J Food Eng 117:42–51
Kandala CV, Sundaram J (2014) Nondestructive moisture content determination of three different market type in-shell peanuts using near infrared reflectance spectroscopy. Food Meas 8:132–141
Kays SE, Windham WR, Barton FE (1998) Prediction of total dietary fiber by near-infrared reflectance spectroscopy in high-fat- and high-sugar-containing cereal products. J Agric Food Chem 46(3):854–861
King J, O’Farrel WV (1997) SFE-new method to measure oil content, inform. 8:1047–1051
Lin P, Chen Y, He Y (2012) Identification of geographical origin of olive oil using visible and near-infrared spectroscopy technique combined with chemometrics. Food Bioprocess Technol 5(1):235–242
Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL, Blasco J (2012) Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol 5(4):1121–1142
Petisco C, Garcia-criado B, Vazquez-de-aldana BR et al (2010) Measurement of quality parameters in intact seeds of Brassica species using visible and near-infrared spectroscopy. Ind Crop Prod 32(2):139–146
Phan-Thien KY, Golic M, Wright GC, Lee NA (2011) Feasibility of estimating peanut essential minerals by near infrared reflectance spectroscopy. Sens & Instrumen Food Qual 5:43–49
Rinnan Å, van den Berg F, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal Chem 28(10):1201–1222
Rustom IYS, Lopez-Leiva MH, Nair BM (1996) Nutritional, sensory and physicochemical properties of peanut beverage sterilized under two different UHT conditions. Food Chem 56(1):45–53
Sanders TH (1980) Fatty acid composition of lipid classes in oils from peanuts differing in variety and maturity. J Am Oil Chem Soc 57(1):12–15
Serranti S, Cesare D, Marini F, Bonifazi G (2013) Classification of oat and groat kernels using NIR hyperspectral imaging. Talanta 103:276–284
Shahin MA, Symons SJ (2012) Detection of fusarium damage in Canadian wheat using visible/near-infrared hyperspectral imaging. Food Meas 6:3–11
Shewfelt AL, Young CT (1977) Storage stability of peanut-based foods: a review. J Food Sci 42(5):1148–1152
Sundaram J, Kandala CV, Ronald AH, Christopher LB, William RW (2010) Determination of in-shell peanut oil and fatty acid composition using near-infrared reflectance spectroscopy. J Am Oil Chem Soc 87:1103–1114
Tavallaie R, Talebpour Z, Azad J, Soudi MR (2011) Simultaneous determination of pyruvate and acetate levels in xanthan biopolymer by infrared spectroscopy: effect of spectral pre-processing for solid-state analysis. Food Chem 124(3):1124–1130
Tillman LB, Gorbet WD, Person G (2006) Predicting oleic and linoleic acid content of single peanut seeds using near-infrared reflectance spectroscopy. Crop Sci 46:2121–2126
Wang L, Wang Q, Liu HZ, Liu L, Du Y (2013) Determining the contents of protein and amino acids in peanuts using near-infrared reflectance spectroscopy. Soc Chem Ind 93:118–124
Weinstock BA, Janni J, Hagen L, Wright S (2006) Prediction of oil and oleic acid concentrations in individual corn (Zea mays l.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis. Appl Spectrosc 60(1):9–16
Williams P, Geladi P, Fox G, Manley M (2009) Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Anal Chim Acta 653:121–130
Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 8(2):109–130
Wu D, He Y, Nie P, Cao F, Bao Y (2010) Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Anal Chim Acta 659(1):229–237
Wu D, Sun DW, He Y (2012) Application of long-wave near infrared hyperspectral imaging for measurement of colour distribution in salmon fillet. Innovative Food Sci Emerg Technol 16:361–372
Zhang XL, Liu F, He Y, Li XL (2012) Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds. Sensors 12:17234–17246
Zheng CX, Sun DW, Zheng LY (2006) Recent developments and applications of image features for food quality evaluation and inspection—a review. Trends Food Sci Technol 17:642–655
Acknowledgments
This research was financially supported by the Key Science and Technology Program of Henan Province (No.1121023103795).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of Interest
Huali Jin declares that she has no conflict of interest. Yongsheng Ma declares that he has no conflict of interest. Linlin Li declares that she has no conflict of interest. Jun-Hu Cheng declares that he has no conflict of interest.
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed Consent
Not applicable.
Rights and permissions
About this article
Cite this article
Jin, H., Ma, Y., Li, L. et al. Rapid and Non-destructive Determination of Oil Content of Peanut (Arachis hypogaea L.) Using Hyperspectral Imaging Analysis. Food Anal. Methods 9, 2060–2067 (2016). https://doi.org/10.1007/s12161-015-0384-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12161-015-0384-3