Determination of Pear Internal Quality Attributes by Fourier Transform Near Infrared (FT-NIR) Spectroscopy and Multivari
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Determination of Pear Internal Quality Attributes by Fourier Transform Near Infrared (FT-NIR) Spectroscopy and Multivariate Analysis Hui Jiang & Weixing Zhu
Received: 21 March 2012 / Accepted: 13 July 2012 / Published online: 25 July 2012 # Springer Science+Business Media, LLC 2012
Abstract This paper attempted the feasibility to determine firmness and soluble solid content (SSC) in intact pears using Fourier transform near infrared (FT-NIR) spectroscopy coupled with multivariate analysis. Principal component analysis and independent component analysis were employed comparatively to extract latent vectors from the original spectra data. Extreme learning machine (ELM) was performed to calibrate regression model. Some parameters of ELM model were optimized according to the lowest root mean square error of cross-validation in the calibration set. Moreover, the root mean square error of prediction of the calibration model was finally corrected for making it more closed to the true prediction error due to the effect of reference measurement error existing in the pear sample attribute value on the prediction error of the model. Experimental results showed that the R2p and ratio performance deviation (RPD) in the prediction set were achieved as follows: R2p 00.81 and RPD02.28 for the firmness model when ICs06 and R2p 00.91 and RPD03.43 for the SSC model when ICs05. This study demonstrates that the predictive precision of the calibration model can be effectively enhanced in measurement of firmness and SSC in intact pears by use of FT-NIR spectroscopy combined with appropriate chemometrics methods. Keywords Fourier transform near infrared spectroscopy . Extreme learning machine . Latent vectors extraction . Internal quality attributes . Intact pear
H. Jiang (*) : W. Zhu (*) School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China e-mail: [email protected] e-mail: [email protected]
Introduction In China, the pear fruit production is one of the largest in the fruit market, but the processing technique and postharvest both are on low levels (Kou et al. 2012; Li et al. 2010). Attributes such as soluble solid content (SSC) and especially firmness are the major internal quality indicators of intact pears (Nicolaï et al. 2008). But determination of these properties is still largely destructive, time-consuming, and costly. Rapid and correct assessment of fresh fruit quality at harvest and after storage could raise the products prices by removing low-quality fruits. Therefore, the development of a reliable, non-destructive method for the quality measurement of intact pears at postharvest is critical to the success of the pear fruit industry. Near infrared (NIR) spectroscopy is a rapid, accurate, and non-invasive detection technique, and it has been proved to be a powerful analytical tool used in fruit productions, such as orange (Liu et al. 2010), apple (McGlone et al. 2002; Xiaobo et al. 2007), peach (Carlomagno et al. 2004), grape (Chauchard et al. 2004; Ferrer-Gallego et al. 2011), and so
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