Rapid Determination of the Main Organic Acid Composition of Raw Japanese Apricot Fruit Juices Using Near-Infrared Spectroscopy

Abstract

The potential of near-infrared (NIR) spectroscopy to rapidly determine citric and malic acid contents of raw Japanese apricot (Japanese "ume", also known as the Japanese plum) fruit juice was investigated. In total, 314 raw juice samples with different organic acid compositions were collected over a long growth period, and spectra (1100-1850 nm) of these samples were acquired using an NIR spectrophotometer with a 1-mm path length. Calibrations were performed using a partial least-squares regression method based on a calibration sample set (211 samples), while validations were performed based on a validation sample set (103 samples). The results revealed good agreement between NIR spectroscopy and capillary electrophoresis, including the correlation coefficient (r2), standard error of prediction (SEP), and bias; no statistically (p = 0.05) significant differences were found for these parameters. Moreover, standard deviation ratios of reference data in the validation sample set to the SEP were higher than 3, indicating that NIR spectroscopy may represent an acceptable method for quantitative evaluation of citric and malic acids in raw Japanese apricot fruit juice.

Near Infrared Spectroscopy for Determination of the Protein Composition of Rice Flour

“…To determine the usefulness of the calibration models, the ratio of the standard deviation of the reference data of the prediction sample set to the SEP (RPD) is usually utilized (Williams 2001;Chen et al 2005Chen et al , 2006. A RPD value of more than 2.5 is regarded as adequate for screening.…”

Rapid Determination of Degradation of Frying Oil Using Near-Infrared Spectroscopy

“…One of the earliest methods for removing baseline offset and slope is the use of derivative spectra (Duckworth, 2004, Chen et al, 2006. The first derivative of a spectrum is simply a measure of the slope of the spectral curve at every point.…”

Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

“…Thus, LV could reduce the dimensionality and compress the original spectral data and explain the variance of the original spectral data related to the chemical constituents. The regression coefficients obtained by PLS analysis are helpful to find which variables were relevant and important for the prediction of Y-variables and to obtain the calibration models (Chen et al 2006;Liu et al 2011).…”