Quantification and Classification of Ethylene Production of Tomatoes with Different Genes Using Near-infrared Diffuse Reflectance Spectroscopy

Abstract

Ethylene is a gaseous plant hormone which gives cues to developmental processes such as fruit ripening in plants. Ethylene content was determined using a gas chromatograph Model SP6800 equipped with a flame ionization detector and an alumina column in this study. The near-infrared (NIR) diffuse reflectance spectroscopy of tomatoes with different genes were collected with an FT-NIR spectrometer system fitted with an optic fiber cable. Partial least-squares regression (PLSR) method was used to develop calibration models to investigate which wavelength was better for predicting ethylene production. Partial least-squares discriminant analysis (PLSDA) was performed to classify ethylene production according to tomatoes with different genes. Different spectral pretreatment methods, such as the first and second derivative, were used. The optimum number of factors used in PLSR was determined by lowest value of predicted residual error sum of squares (PRESS). Calibration statistics included correlation coefficient (r), root mean square error of calibration (RMSEC), and root mean square error of cross validation (RMSECV). The results show that NIR diffuse reflectance spectroscopy can be used to predict ethylene production. This study also indicates that differences of ethylene content produced by tomatoes with different genes do exist and NIR spectroscopy has a potential to classify tomatoes according to ethylene production.