Nondestructive estimation of maturity and textural properties on tomato %26apos;Momotaro%26apos; by near infrared spectroscopy

作者：Sirisomboon Panmanas; Tanaka Munehiro^*; Kojima Takayuki; Williams Phil

来源：Journal of Food Engineering, 2012, 112(3): 218-226.

DOI：10.1016/j.jfoodeng.2012.04.007

• Tomato
• Momotaro
• Maturity
• Textural properties
• Near infrared spectroscopy
• Principle component analysis (PCA)
• Soft independent modeling of class analogy (SIMCA)
• Partial least square (PLS) regression

Near infrared spectroscopy offers the possibility to classify and predict the internal quality of fruits and vegetables. The objective of this study was to evaluate the ability of near infrared spectroscopy to classify the maturity level and to predict textural properties of tomatoes variety %26quot;Momotaro%26quot;. Principal component analysis (PCA) and Soft independent modeling of class analogy (SIMCA) were used to distinguish among different maturities (mature green, pink and red). Partial least squares (PLS) regression was used to estimate textural properties, alcohol insoluble solids and soluble solids content of the tomatoes. The PCA calibration model with mean normalization pretreatment spectra of mature green tomatoes, gave the highest distinguishability (96.85%). It could classify 100.00% of red and pink tomatoes. The SIMCA model could not give better accuracy in maturity classification than individual PCA models. Among the textural parameters measured, the bioyield force from the puncture test with the near infrared (NIR) spectra (between 1100 and 1800 nm) pretreated by multiplicative scatter correction (MSC) had the highest correlation coefficient between NIR predicted and reference values (r = 0.95) and lowest standard error of prediction (SEP = 0.35 N) and bias of 0.19 N. The ratio of standard deviation of reference data of prediction set to standard error of prediction (RPD) was 2.71. In the case of Momotaro tomato, NIR spectroscopy by using PLS regression could not predict alcohol insoluble solids in fresh weight accurately but could predict soluble solids content well with r of 0.80, SEP of 0.210 %Brix and bias of 0.022 %Brix.

• 出版日期2012-10