[Discrimination of Lactarius and Russula Mushrooms with FTIR and Two-Dimensional Correlation Infrared Spectroscopy].

Tri-step infrared spectroscopy method of Fourier transform infrared spectroscopy, second derivative infrared spectroscopy and two-dimensional correlation infrared spectroscopy was firstly used to discriminate six species of mushrooms belonging to the genus Lactarius and Russula. The absorption bands of the original spectrum were very similar, which were composed by protein and polysaccharides, but tiny differences were observed at the position, shape and absorption intensities of peaks. Second derivative infrared spectroscopy technology was applied to study 6 species of the samples, there were obvious differences in the range of 1 800～1 400 and 1 200～800 cm-1. Two-dimensional correlation infrared spectroscopy can improve the resolution of spectra. Therefore two-dimensional correlation infrared spectroscopy was used to study 6 kinds of mushrooms. The results showed that there are three auto-peaks in the Lactarius, four in the Russula and significant differences in the number, intensity of auto-peaks and cross peaks were observed in the range of 1 690~1 420 cm-1. In addition, the peaks quantity, position, intensity of auto-peaks and cross peaks were different in the range of 1 110~920 cm-1. It demonstrates that tri-step infrared spectroscopy technology of Fourier transform infrared spectroscopy, second derivative infrared spectroscopy and two-dimensional correlation infrared spectroscopy is a rapid and effective method for discriminating Lactarius and Russula.

[Discrimination of Seven Species of Boletus with Fourier Transform Infrared Spectroscopy].

Fourier transform infrared spectroscopy, two-dimensional correlation infrared spectroscopy and principal component analysis were used to discriminate seven species of boletus belonging to the same genus. The results showed that the absorption bands of original spectra were similar, which were mainly composed of the absorption bands of protein and polysaccharides, but tiny differences were still observed at the position and intensities of peaks. Two-dimensional correlation infrared spectroscopy technology was applied to study the sample. It showed that there are 6 auto-peaks in the Boletus brunneissimus Chiu and Boletus bicolor, 5 auto-peaks in the Boletus speciosus, 4 auto-peaks in the Boletus griseus Forst and Boletus calopus, only 3 in the Boletus edulis and Boletus aereus in the range of 1 680~1 300 cm-1. The significant differences in the position, intensity of auto-peaks and cross peaks were still observed in the range of 1 680~1 300 cm-1. Same significant differences were observed in the range of 1 150~920 cm-1. Principal component analysis was conducted on boletus with second derivative infrared spectra in the range of 1 800~800 cm-1. All the samples were distinguished and the classification accuracy of principal component analysis is up to 100%. It is demonstrated that Fourier transform infrared spectroscopy combined with two-dimensional correlation infrared spectroscopy or principal component analysis is a rapid and effective method for discriminating mushrooms.

[Analysis of different types of soil by FTIR and ICP-MS].

Fourier transform infrared spectroscopy (FTIR) and inductively coupled plasma mass spectrometry (ICP-MS) were used to study six types of farmland soil from different areas. The FTIR results showed that the infrared spectra of soil were mainly composed of the absorption band of clay minerals, organic matter and inorganic salts, such as carbonate, phosphate, manganate and so on. The mineral atlas of six soil samples were all of montmorillonite type. The ICP-MS test results showed that the available elements content of different types and colours of soil samples were different There was significant lack status of available Ca between different types of farmland soil, the content of available Mg in Huludao soil was in the medium level, other areas were in the status of shortage. There was only significant lack status of available Mn and available Zn in Baiyin soil, the content of available Fe in Chenggong soil was in the status of shortage, the content of available Cu in all areas was particularly rich. The content of available P in Jining soil was rich, Luoyang and Huludao soil were in the medium level, the soil of Chenggong, Baiyin and Luliang were in the status of shortage. The content of available K in Luoyang, Chenggong and Jining soil was relatively rich, Luliang soil was in the medium level, the soil of Huludao and Baiyin were in the status of shortage. It is observed that the deeper the color of soil samples, the richer the amount of some available trace elements such as magnesium, copper, iron, manganese and zinc. According to the national classification standard of available elements content, we analyzed the nutrients of available elements content in the farmland soil of different areas, and implemented remedial measures for the lacking of available elements for all of the six areas.

[FTIR study of the influence of leaf senescence on magnoliaceae cluster analysis].

Fourier transform infrared (FTIR) spectroscopy combined with hierarchical cluster analysis was used to study the influence of leaf senescence on magnoliaceae cluster. FTIR spectra of young, mature and old yellow leaves were obtained from 14 species trees belonging to the three magnoliaceae subtribes. Results showed that the infrared spectra of the three subtribes plant leaves were similar, only with minor differences in the absorption intensity of several peaks. Hierarchical cluster analysis was performed on the second derivative infrared spectra in the range 1800-700 cm(-1). The HCA results showed that the cluster based on mature leaves is better than that based on young and old yellow leaves. Our study suggests that it should be cautious to select leaf sample while using leaf spectra for classification.

[Infrared spectroscopic study on leaf senescence of evergreen tree].

In order to investigate plant physiological process of leaf senescence and aging, Fourier transform infrared (FTIR) spectroscopy was used to study the young, mature, and old yellow leaves from seven species of evergreen trees. The spectra of the leaves from different growing period are different in the region of 1 800-700 cm(-1). The absorption ratios A1 070/A2 927, A1 070/A1 160 were used to evaluate the relative changes of polysaccharides, and A1 318/A2 922 was used to estimate the change of calcium oxalate during leaf senescence. Decomposition and curve-fitting analysis was performed in the region of 1 800 -1 500 cm(-1). The sub-band absorption ratio H1 650/H1 740 was used to evaluate the relative changes of protein in the leaves. The results show that the accumulation and mobilization of polysaccharides, protein, and calcium oxalate during leaf growing period were different in different plant species. This study demonstrates the potential of mid-infrared spectroscopy for investigation of plants senescence, as well as physiological and biochemical changes of plants.

[Discrimination of bamboo using FTIR spectroscopy and statistical analysis].

Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to identify and classify bamboo leaves. FTIR spectra of fifty-four bamboo leaf samples belonging to six species were obtained. The results showed that the infrared spectra of bamboo leaves were similar, and mainly composed of the bands of polysaccharides, protein and lipids. The original spectra exhibit minor differences in the region of 1800-700cm-1. The second derivative spectra show apparent differences in the same region. Principal component analysis and hierarchical cluster analysis were performed on the second derivative infrared spectra in the range from 1800 to 700 cm-1. The leaf samples were separated into 6 groups with accuracy of 98% with the first three principal components, and with 100% accuracy according to the third and fourth principal components. Hierarchical cluster analysis can correctly cluster the bamboo leaf samples. It is proved that Fourier transform infrared spectroscopy combined with PCA and HCA could be used to discriminate bamboo at species level with only a tiny leaf sample.

[Research on rice blast, corn and broad bean rust leaves by FTIR spectroscopy].

Fourier transform infrared Spectroscopy (FTIR) was used to study healthy and diseased leaves of rice, corn, and broad bean. The results show that the infrared spectra of the leaves are mainly composed of the absorption bands of proteins and polysaccharide. The spectra are similar, but the differences were observed in the peak positions, shapes, and absorption ratios. The remarkable differences in correlation coefficients were also observed in the region of 1 800-1 000 cm(-1) of the second derivate spectra. The absorption ratio A1 056 /A1 652 of healthy rice leaves and rice blast leaves, A1 652/A2 920 of healthy broad bean leaves and leaves, A1 056/A2 920 of healthy corn and corn rust (non-lesion site), and A1 652/A2 920 of healthy corn and corn rust (lesion site) all show a decline from healthy to diseased leaves, showing that the contents of polysaccharide and the protein have some differences.

[FTIR spectroscopic study of broad bean diseased leaves].

In the present paper, broad bean rust, fusarium rhizome rot, broad bean zonate spot, yellow leaf curl virus and normal leaves were studied using Fourier transform infrared spectroscopy combined with chemometrics. The results show that the spectra of samples were similar, only with minor differences in absorption intensity of several peaks. Second derivative analyses show that the significant difference of all samples was in the range of 1200-700 cm(-1). The data in the range of 1 200-700 cm(-1) were selected to evaluate correlation coefficients, hierarchical cluster analysis (HCA) and principal component analysis (PCA). Results showed that the correlation coefficients are larger than 0.928 not only between the healthy leaves, but also between the same diseased leaves. The values between healthy and diseased leaves, and among diseased leaves, are all declined. HCA and PCA yielded about 73.3% and 82.2% accuracy, respectively. This study demonstrated that FTIR techniques might be used to detect crop diseases.