Using Vis/NIR Diffuse Transmittance Spectroscopy and Multivariate Analysis to Predicate Soluble Solids Content of Apple

详细信息    查看全文

• 作者：Shuxiang Fan ; Zhiming Guo ; Baohua Zhang ; Wenqian Huang…
• 关键词：Apple ; Soluble solids content ; Fruit orientation ; Diffuse transmittance ; Variable selection ; Area change rate
• 刊名：Food Analytical Methods
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：9
• 期：5
• 页码：1333-1343
• 全文大小：583 KB
• 参考文献：Andersen CM, Bro R (2010) Variable selection in regression—a tutorial. J Chemom 24:728–737CrossRef
  Araújo MCU, Saldanha TCB, Galvão RKH, Yoneyama T, Chame HC, Visani V (2001) The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom Intell Lab Syst 57:65–73CrossRef
  Barnes R, Dhanoa M, Lister SJ (1989) Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc 43:772–777CrossRef
  Cai W, Li Y, Shao X (2008) A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemom Intell Lab Syst 90:188–194CrossRef
  Cen H, He Y (2007) Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci Technol 18:72–83CrossRef
  Demetriades-Shah TH, Steven MD, Clark JA (1990) High resolution derivative spectra in remote sensing. Remote Sens Environ 33:55–64CrossRef
  Do Trong NN, Erkinbaev C, Tsuta M, De Baerdemaeker J, Nicolaï B, Saeys W (2014) Spatially resolved diffuse reflectance in the visible and near-infrared wavelength range for non-destructive quality assessment of ‘Braeburn’ apples. Postharvest Biol Technol 91:39–48CrossRef
  Durand A, Devos O, Ruckebusch C, Huvenne J (2007) Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton–viscose textiles. Anal Chim Acta 595:72–79CrossRef
  Fan G, Zha J, Du R, Gao L (2009) Determination of soluble solids and firmness of apples by Vis/NIR transmittance. J Food Eng 93:416–420CrossRef
  Fan S, Huang W, Guo Z, Zhang B, Zhao C (2015) Prediction of soluble solids content and firmness of pears using hyperspectral reflectance imaging. Food Anal Methods 8:1936–1946CrossRef
  Fu X, Ying Y (2014) Food safety evaluation based on near infrared spectroscopy and imaging: a review. Crit Rev Food Sci Nutr (just accepted)
  Fu X, Ying Y, Lu H, Xu H (2007) Comparison of diffuse reflectance and transmission mode of visible-near infrared spectroscopy for detecting brown heart of pear. J Food Eng 83:317–323CrossRef
  Giovanelli G, Sinelli N, Beghi R, Guidetti R, Casiraghi E (2014) NIR spectroscopy for the optimization of postharvest apple management. Postharvest Biol Technol 87:13–20CrossRef
  Helland IS, Næs T, Isaksson T (1995) Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data. Chemom Intell Lab Syst 29:233–241CrossRef
  Huang H, Yu H, Xu H, Ying Y (2008) Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. J Food Eng 87:303–313CrossRef
  Huang M, Wang Q, Zhang M, Zhu Q (2014) Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology. J Food Eng 128:24–30CrossRef
  Jamshidi B, Minaei S, Mohajerani E, Ghassemian H (2012) Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges. Comput Electron Agric 85:64–69CrossRef
  Jie D, Xie L, Fu X, Rao X, Ying Y (2013) Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique. J Food Eng 118:387–392CrossRef
  Jie D, Xie L, Rao X, Ying Y (2014) Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system. Postharvest Biol Technol 90:1–6CrossRef
  Leiva-Valenzuela GA, Lu R, Aguilera JM (2013) Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. J Food Eng 115:91–98CrossRef
  Li H, Liang Y, Xu Q, Cao D (2009) Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal Chim Acta 648:77–84CrossRef
  Li J, Huang W, Zhao C, Zhang B (2013) A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. J Food Eng 116:324–332CrossRef
  Li J, Huang W, Chen L, Fan S, Zhang B, Guo Z, Zhao C (2014) Variable selection in visible and near-infrared spectral analysis for noninvasive determination of soluble solids content of ‘Ya’ Pear. Food Anal Methods 7:1891–1902CrossRef
  Liu Y, Ying Y (2005) Use of FT-NIR spectrometry in non-invasive measurements of internal quality of ‘Fuji’ apples. Postharvest Biol Technol 37:65–71CrossRef
  Liu Y, Ying Y, Fu X, Lu H (2007) Experiments on predicting sugar content in apples by FT-NIR technique. J Food Eng 80:986–989CrossRef
  Liu F, Jiang Y, He Y (2009) Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: a case study to determine soluble solids content of beer. Anal Chim Acta 635:45–52CrossRef
  Liu Y, Sun X, Ouyang A (2010) Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Sci Technol 43:602–607CrossRef
  Liu D, Sun D-W, Zeng X-A (2014) Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food Bioprocess Technol 7:307–323CrossRef
  Lu R (2004) Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biol Technol 31:147–157CrossRef
  Mendoza F, Lu R, Cen H (2014) Grading of apples based on firmness and soluble solids content using Vis/SWNIR spectroscopy and spectral scattering techniques. J Food Eng 125:59–68CrossRef
  Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46:99–118CrossRef
  Peirs A, Lammertyn J, Ooms K, Nicolaï BM (2001) Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR-spectroscopy. Postharvest Biol Technol 21:189–199CrossRef
  Peng Y, Lu R (2007) Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images. J Food Eng 82:142–152CrossRef
  Perkins J, Tenge B, Honigs D (1988) Resolution enhancement using an approximate-inverse Savitzky-Golay smooth. Spectrochim Acta B 43:575–603CrossRef
  Qing Z, Ji B, Zude M (2007) Wavelength selection for predicting physicochemical properties of apple fruit based on near-infrared spectroscopy. J Food Quality 30:511–526CrossRef
  Savitzky A, Golay MJ (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639CrossRef
  Schmutzler M, Huck CW (2014) Automatic sample rotation for simultaneous determination of geographical origin and quality characteristics of apples based on near infrared spectroscopy (NIRS). Vib Spectrosc 72:97–104CrossRef
  Smith BC (2003) Quantitative spectroscopy: theory and practice. Elsevier Science, Academic Press, pp. 66
  Sun T, Lin H, Xu H, Ying Y (2009) Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biol Technol 51:86–90CrossRef
  Wang A, Xie L (2014) Technology using near infrared spectroscopic and multivariate analysis to determine the soluble solids content of citrus fruit. J Food Eng 143:17–24CrossRef
  Xiaobo Z, Jiewen Z, Xingyi H, Yanxiao L (2007) Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of ‘Fuji’ apple based on different PLS models. Chemom Intell Lab Syst 87:43–51CrossRef
  Xie L, Ye X, Liu D, Ying Y (2011) Prediction of titratable acidity, malic acid, and citric acid in bayberry fruit by near-infrared spectroscopy. Food Res Int 44:2198–2204CrossRef
  Xu H, Qi B, Sun T, Fu X, Ying Y (2012) Variable selection in visible and near-infrared spectra: application to on-line determination of sugar content in pears. J Food Eng 109:142–147CrossRef
  Yun YH et al (2014) A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration. Anal Chim Acta 807:36–43CrossRef
  Zhang L, Xu H, Gu M (2014) Use of signal to noise ratio and area change rate of spectra to evaluate the Visible/NIR spectral system for fruit internal quality detection. J Food Eng 139:19–23CrossRef
  Zhu Q, Huang M, Zhao X, Wang S (2013) Wavelength selection of hyperspectral scattering image using new semi-supervised affinity propagation for prediction of firmness and soluble solid content in apples. Food Anal Methods 6:334–342CrossRef
  
• 作者单位：Shuxiang Fan (1) (2) (3) (4)
  Zhiming Guo (1) (2) (3) (4)
  Baohua Zhang (1) (2) (3) (4)
  Wenqian Huang (1) (2) (3) (4)
  Chunjiang Zhao (1) (2) (3) (4)
  
  1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, 100097, China
  2. National Research Center of Intelligent Equipment for Agriculture, Beijing, 100097, China
  3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, 100097, China
  4. Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing, 100097, China
  
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Food Science
  Chemistry
  Microbiology
  Analytical Chemistry
  
• 出版者：Springer New York
• ISSN：1936-976X

文摘

The objectives of this research were to compare the effect of different fruit orientations on the quality of acquired spectra and to provide a suitable calibration model for further online determination of soluble solids content (SSC) of “Fuji” apples using visible and near-infrared (Vis/NIR) diffuse transmittance. The diffuse transmittance spectra between 650 and 910 nm were collected with the designed spectrum measurement system in two fruit orientations: stem-calyx axis horizontal (T1) and stem-calyx axis vertical (T2). Area change rate (ACR) was used to evaluate the stability of spectra collected in two fruit orientations. Results showed that the fruit orientation T1 was better for our designed spectrum measurement system. Then, the performance of partial least squares (PLS) models based on spectral data after the pretreatment of several preprocessing methods was analyzed and compared. Finally, the modified competitive adaptive reweighted sampling (MCARS), successive projection algorithm (SPA), and their combination were investigated to select the effective variables for the determination of SSC. It concluded that the MCARS-SPA-PLS model based on the spectra after preprocessing of Savitzky-Golay (SG) smoothing achieved better results for SSC prediction. The correlation coefficients between measured and predicted SSC were 0.962 and 0.946, and the root mean square errors were 0.510 and 0.527°Brix for calibration and prediction set, respectively. Moreover, the physicochemical properties of 27 variables selected by MCARS-SPA were discussed to obtain a better interpretation of the calibration model. The overall results indicated that the designed diffuse transmittance spectrum measurement system together with the PLS calibration model with 27 effective variables selected by MCARS-SPA method had a potential application for online SSC detection of apple.