Xiaoying Niu

Determination of amino acids in Chinese rice wine by Fourier transform near-infrared spectroscopy.

Chinese rice wine is abundant in amino acids. The possibility of quantitative detection of 16 free amino acids (aspartic acid, threonine, serine, glutamic acid, proline, glycine, alanine, valine, methionine, isoleucine, leucine, tyrosine, phenylalanine, lysine, histidine, and arginine) in Chinese rice wine by Fourier transform near-infrared (NIR) spectroscopy was investigated for the first time in this study. A total of 98 samples from vintage 2007 rice wines with different aging times were analyzed by NIR spectroscopy in transmission mode. Calibration models were developed using partial least-squares regression (PLSR) with high-performance liquid chromatography (HPLC) by postcolumn derivatization and diode array detection as a reference method. To validate the calibration models, full cross (leave-one-out) validation was employed. The results showed that the calibration statistics were good (rcal>0.94) for all amino acids except proline, histidine, and arginine. The correlation coefficient in cross validation (rcv) was >0.81 for 12 amino acids. The residual predictive deviation (RPD) value obtained was >1.5 in all amino acids except proline and arginine, and it was >2.0 in 6 amino acids. The results obtained in this study indicated that NIR spectroscopy could be used as an easy, rapid, and novel tool to quantitatively predict free amino acids in Chinese rice wine without sophisticated methods.

Analysis of Sugars in Chinese Rice Wine by Fourier Transform Near-Infrared Spectroscopy with Partial Least-Squares Regression

The feasibility of rapid analysis for oligosaccharides, including isomaltose, isomaltotriose, maltose, and panose, in Chinese rice wine by Fourier transform near-infrared (FT-NIR) spectroscopy together with partial least-squares regression (PLSR) was studied in this work. Forty samples of five brewing years (1996, 1998, 2001, 2003, and 2005) were analyzed by NIR transmission spectroscopy with seven optical path lengths (0.5, 1, 1.5, 2, 2.5, 3, and 5 mm) between 800 and 2500 nm. Calibration models were established by PLSR with full cross-validation and using high-performance anion-exchange chromatography coupled with pulsed amperometric detection as a reference method. The optimal models were obtained through wavelength selection, in which the correlation coefficients of calibration (r(cal)) for the four sugars were 0.911, 0.938, 0.925, and 0.966, and the root-mean-square errors of calibrations were 0.157, 0.147, 0.358, and 0.355 g/L, respectively. The validation accuracy of the four models, with correlation coefficients of cross-validation (r(cv)) being 0.718, 0.793, 0.681, and 0.873, were not very satisfactory. This might be due to the low concentrations of the four sugars in Chinese rice wine and the influence of some components having structures similar to those of the four sugars. The results obtained in this study indicated that the NIR spectroscopy technique offers screening capability for isomaltose, isomaltotriose, maltose, and panose in Chinese rice wine. Further studies with a larger Chinese rice wine sample should be done to improve the specificity, prediction accuracy, and robustness of the models.

Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy

The near infrared (NIR) spectroscopy technique has been applied in many fields because of its advantages of simple preparation, fast response, and non-destructiveness. We investigated the potential of NIR spectroscopy in diffuse reflectance mode for determining the soluble solid content (SSC) and acidity (pH) of intact loquats. Two cultivars of loquats (Dahongpao and Jiajiaozhong) harvested from two orchards (Tangxi and Chun’an, Zhejiang, China) were used for the measurement of NIR spectra between 800 and 2500 nm. A total of 400 loquats (100 samples of each cultivar from each orchard) were used in this study. Relationships between NIR spectra and SSC and acidity of loquats were evaluated using partial least square (PLS) method. Spectra preprocessing options included the first and second derivatives, multiple scatter correction (MSC), and the standard normal variate (SNV). Three separate spectral windows identified as full NIR (800∼2500 nm), short NIR (800∼1100 nm), and long NIR (1100∼2500 nm) were studied in factorial combination with the preprocessing options. The models gave relatively good predictions of the SSC of loquats, with root mean square error of prediction (RMSEP) values of 1.21, 1.00, 0.965, and 1.16 °Brix for Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun’an-Dahongpao, and Chun’an-Jiajiaozhong, respectively. The acidity prediction was not satisfactory, with the RMSEP of 0.382, 0.194, 0.388, and 0.361 for the above four loquats, respectively. The results indicate that NIR diffuse reflectance spectroscopy can be used to predict the SSC and acidity of loquat fruit.

Non-invasive determination of enological parameters of rice wine by Vis-NIR spectroscopy and least-squares support vector machines

The feasibility of visible and near-infrared (Vis-NIR) spectroscopy together with least-squares support vector machines (LS-SVM) for non-destructive determination of rice wine composition in coloured bottles was investigated. The Vis-NIR spectra were collected in a fiber spectrometer system with the bottle sealed and the labels removed. Statistical equations were established between the reference data and Vis-NIR spectra by partial least squares regression (PLSR) and LS-SVM, respectively. The prediction performance of LV-SVM is superior to that of PLSR, with higher rval of 0.960, 0.942 and 0.866, and lower RMSEP of 0.115 % (V V-1), 0.071 g L-1 and 0.021 for alcohol content, titratable acidity and pH, respectively. Based on the results, it was concluded that the Vis-NIR spectrometer system was suitable for non-destructive rice wine quality determination, and LS-SVM was a reliable multivariate method for rice wine composition prediction.

Near-infrared transmittance spectroscopy for nondestructive determination of soluble solids content and pH in tomato juice

The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids contents (SSC) and pH in tomato juices was investigated. A total of 200 tomato juice samples were used for NIR spectroscopy analysis at 800-2400 nm using FT-NIR spectrometer. Multiplicative signal correcton (MSC), the first and second derivative were applied for preprocessing spectral data. The relationship between SSC, pH and FT-NIR spectra of tomato juice was analyzed via partial least-squares (PLS) regression, respectively. PLS regression models for SSC and pH in tomato juices show the high accuracy. The correlation coefficient (r), root mean square error of calibration (RMSEC) and root mean square error of validation (RMSEP), root mean square error of cross-validation (RMSECV) for SSC were 0.91582, 0.0703, 0.150 and 0.138, respectively, whereas those values for pH were 0.8997, 0.0333, 0.0316 and 0.0489, respectively. It is concluded that the NIR transmittance spectroscopy is promising for the fast and nondestructive detection of chemical components in tomato juices.

Study on the oxidation process of tomato juice during storage by near-infrared spectroscopy

Near-infrared (NIR) transmittance spectroscopy combined with several chemometrical techniques was investigated to study the oxidation process during storage in tomato juices. A total of 100 tomato juice samples were used for NIR spectroscopy analysis at 800-2400 nm using FT-NIR spectrometer. The spectrum of each tomato juice was collected twice: the first time as soon as the tomatoes were squeezed, centrifuged, filtered and the tomato juice had not undergone any oxidation process and the second measurement was taken after a month. Principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were applied to discriminate between the two groups of spectra. The results show that differences between tomato juices before and after the storage period do exist attributed to changes in certain compounds of juice and excellent classification can be obtained after optimizing spectral pretreatment.

Effect of Optical Path Length on Prediction Precision of Calibration Models Developed for Enological Parameters of Rice Wine

Optical path length is one of the most important factors in Beer-Lambert Law which is the basis of nearly all multivariate calibration methods for spectroscopic data. The effect of optical path length on the near infrared (NIR) spectra of rice wine and the consequences on the prediction precision of calibration models developed for measuring total sugar, non-sugar solid, alcohol content, titratable acidity, amino acid nitrogen, and pH was investigated. Samples were scanned in transmission mode using a commercial FT-NIR spectrometer and a demountable liquid cell for versatile path length liquid sampling. Eleven optical path lengths were studied: 0.5 mm, 0.75 mm, 1.0 mm, 1.25 mm, 1.5 mm, 1.75 mm, 2.0 mm, 2.25 mm, 2.5 mm, 2.75 mm, and 3.0 mm. The NIR spectra of rice wine samples in different optical path length show that increasing the optical path length raised the absorption intensity but shortened the wavelength range in which absorbance measurement could be made. And, a linear relationship was observed between the absorption intensity and the optical path length when absorption intensity was lower than 3.0. It could also be observed that optical path length did have influence on the calibration results for the six enological parameters, and the recommended optical path length for total sugar, non-sugar solid alcohol content, titratable acidity, amino acid nitrogen and pH was 0.75 mm, 1 mm, 2 mm, 1.75 mm, 0.5 mm and 2 mm, respectively. The results demonstrated that it was necessary to determine the optimum optical path length to obtain the optimum results for NIR analysis.