[Correlations Between Near Infrared Spectra and Molecular Structures of 20 Standard Amino Acids].

The objective of the research was to study the correlations between near infrared spectra and molecular structures of 20 standard amino acids. It was to establish the theoretical foundation for widely use of the amino acids near infrared spectra in animal science, food and medicine. Measurement of the near infrared spectra was performed using a Shimadzu Fourier transform infrared spectrophotometer IRPrestige-21, with FlexIRTM Near-Infrared Fiber Optics module. The spectrometric data acquisitions were performed by Shimadzu IRsolution 1.50 system. The spectrometric room temperature was 25 ℃ and humidity was 38%. Spectra of 20 amino acid standard substances were collected by reflectance mode from 1 000 to 2 502 nm in 8 cm-1 increment. Each sample was scanned in three times, each scan was 50 cycles, and the average value of three times scan result was used for each sample. Based on the differences of amino acids side chains, the correlations between near infrared spectra and molecular structures were compared in the fat family amino acids, aromatic amino acids and heterocycle amino acids. The result shows that all 20 standard amino acids have very specific absorption line patterns. It is distinctly different in these absorption line patterns. Near-infrared spectra of high molecular weight fat family amino acids are affected by side chains. Near-infrared spectra of glycine are affected by carboxyl and amino. The differences of near-infrared spectra between two aromatic amino acids are in benzene ring. ­OH groups on benzene ring of tyrosine lower the symmetry of benzene molecule. It leads to the emergence of more vibration absorption. Near-infrared spectra of heterocycle amino acids are distinctly different in 1 000～2 502 nm because of side chains. In conclusion, there are four different characteristic spectral regions. The first one is 1 050～1 200 nm spectral region which is composed mainly of second-order frequency doubling of C­H group. The second is 1 300～1 500 nm spectral region which is composed mainly of combination tune of C­H group. Due to side chains of amino acid have different molecular structure, they yield a complete set of near infrared fingerprint spectra between 1 600～1 850 and 2 000～2 502 nm. In another words, these four characteristic regions of near infrared spectra can be used to build the model of qualitative analysis and quantitative analysis for amino acid, and improves the accuracy and reliability of model.

[Application of near infrared reflectance spectroscopy to predict meat chemical compositions: a review].

In contrast to conventional methods for the determination of meat chemical composition, near infrared reflectance spectroscopy enables rapid, simple, secure and simultaneous assessment of numerous meat properties. The present review focuses on the use of near infrared reflectance spectroscopy to predict meat chemical compositions. The potential of near infrared reflectance spectroscopy to predict crude protein, intramuscular fat, fatty acid, moisture, ash, myoglobin and collagen of beef, pork, chicken and lamb is reviewed. This paper discusses existing questions and reasons in the current research. According to the published results, although published results vary considerably, they suggest that near-infrared reflectance spectroscopy shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. In particular, under commercial conditions where simultaneous measurements of different chemical components are required, near infrared reflectance spectroscopy is expected to be the method of choice. The majority of studies selected feature-related wavelengths using principal components regression, developed the calibration model using partial least squares and modified partial least squares, and estimated the prediction accuracy by means of cross-validation using the same sample set previously used for the calibration. Meat fatty acid composition predicted by near-infrared spectroscopy and non-destructive prediction and visualization of chemical composition in meat using near-infrared hyperspectral imaging and multivariate regression are the hot studying field now. On the other hand, near infrared reflectance spectroscopy shows great difference for predicting different attributes of meat quality which are closely related to the selection of calibration sample set, preprocessing of near-infrared spectroscopy and modeling approach. Sample preparation also has an important effect on the reliability of NIR prediction; in particular, lack of homogeneity of the meat samples influenced the accuracy of estimation of chemical components. In general the predicting results of intramuscular fat, fatty acid and moisture are best, the predicting results of crude protein and myoglobin are better, while the predicting results of ash and collagen are less accurate.