Haifeng Cui

Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages by Fourier transform infrared (FTIR) spectroscopy and chemometrics.

Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages was developed by Fourier transform infrared (FTIR) spectrometry combined with chemometrics. Transmittance spectra ranging from 400 to 4000 cm⁻¹ of 73 Halal and 78 non-Halal Chinese ham sausages were measured. Sample preparation involved finely grinding of samples and formation of KBr disks (under 10 MPa for 5 min). The influence of data preprocessing methods including smoothing, taking derivatives and standard normal variate (SNV) on partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) was investigated. The results indicate removal of spectral background and baseline plays an important role in discrimination. Taking derivatives, SNV can improve classification accuracy and reduce the complexity of PLSDA. Possibly due to the loss of detailed high-frequency spectral information, smoothing degrades the model performance. For the best models, the sensitivity and specificity was 0.913 and 0.929 for PLSDA with SNV spectra, 0.957 and 0.929 for LS-SVM with second derivative spectra, respectively.

Combining local wavelength information and ensemble learning to enhance the specificity of class modeling techniques: Identification of food geographical origins and adulteration

Class modeling techniques are required to tackle various one-class problems. Because the training of class models is based on the target class and the origins of future test objects usually cannot be exactly predefined, the criteria for feature selection of class models are not very straightforward. Although feature reduction can be expected to improve class models performance, more features retained can provide a sufficient description of the sought-for class. This paper suggests a strategy to balance class description and model specificity by ensemble learning of sub-models based on separate local wavelength intervals. The acceptance or rejection of a future object can be explicitly determined by examining its acceptance frequency by sub-models. Considering the lack of information about sub-model independence, we propose to use a data-driven method to control the sensitivity of the ensemble model by cross validation. In this way, all the wavelength intervals are used for class description and the local wavelength intervals are highlighted to enhance the ability to detect out-of-class objects. The proposed strategy was performed on one-class partial least squares (OCPLS) and soft independent modeling of class analogy (SIMCA). By analysis of two infrared spectral data sets, one for geographical origin identification of white tea and the other for discrimination of adulterations in pure sesame oil, the proposed ensemble class modeling method was demonstrated to have similar sensitivity and better specificity compared with total-spectrum SIMCA and OCPLS models. The results indicate local spectral information can be extracted to enhance class model specificity.

Rapid Discrimination of the Geographical Origins of an Oolong Tea (Anxi-Tieguanyin) by Near-Infrared Spectroscopy and Partial Least Squares Discriminant Analysis

This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

Morphological and Molecular Differences in Two Strains of Ustilago esculenta

Ustilago esculenta is a fungal endophyte of Zizania latifolia that plays an important agricultural role in this vegetable crop. The purpose of this study was to characterize sporidial (T) and mycelial (M-T) strains of U. esculenta isolated from sporulating and non-sporulating galls on plants growing in Zhejiang province, China. Morphological comparisons of the T strain and M-T strain were made by optical and scanning electron microscope observation. Genetic differences were examined by sequencing the ITS region of the fungus and examining differential protein expression by two-dimensional gel electrophoresis and MALDI-TOF-MS/MS. The sporidial (T) and mycelial (M-T) strains differed in morphological characteristics of their in vitro single colony formations and in cell shape. Alignment of ITS sequences of the T strain and M-T strain revealed a single mutation between the T strain and M-T strain, but the sequences were the same within strains. A total of 146 proteins were only expressed in the M-T strain, and 242 proteins were only expressed in the T strain isolated from infected plants. A total of 222 proteins were up-regulated or down-regulated in the T strain when compared with the M-T strain. Of these, 18 proteins were identified and eight were associated with processes involving energy metabolism and the cytoskeleton. Two morphology-related proteins, MAP kinase kinase and actin, were differentially expressed. The differences noted in the T strain and M-T strain may lead to a better understanding of the life cycle and morphogenesis in U. esculenta.

Protected Geographical Indication Identification of a Chinese Green Tea (Anji-White) by Near-Infrared Spectroscopy and Chemometric Class Modeling Techniques

This paper reports a rapid identification method for a Chinese green tea with PGI, Anji-white tea, by class modeling techniques and NIR spectroscopy. 167 real and representative Anji-white tea samples were collected from 8 tea plantations in their original producing areas for model training. Another 81 non-Anji-white tea samples of similar appearance were collected from 7 important tea producing areas and used for validation of model specificity. Diffuse NIR spectra were measured with finely ground tea powders. OCPLS and SIMCA were used to describe the distribution of representative Anji-white tea objects and predict the authenticity of new objects. For data preprocessing, smoothing, derivatives, and SNV were applied to improve the raw spectra and classification performance. It is demonstrated that taking derivatives and SNV can improve classification accuracy and reduce the complexity of class models by removing spectral background and baseline. For the best models, the sensitivity and specificity were 0.886 and 0.951 for OCPLS, 0.886 and 0.938 for SIMCA with SNV spectra, respectively. Although it is difficult to perform an exhaustive analysis of all types of potential false objects, the proposed method can detect most of the important non-Anji-white teas in the Chinese market.

Robust and Automated Internal Quality Grading of a Chinese Green Tea (Longjing) by Near-Infrared Spectroscopy and Chemometrics

Near-infrared (NIR) spectroscopy and chemometric methods were applied to internal quality control of a Chinese green tea, Longjing, with Protected Geographical Indication (PGI). A total of 2745 authentic Longjing tea samples of three different grades were analyzed by NIR spectroscopy. To remove the influence of abnormal samples, The Stahel-Donoho estimate (SDE) of outlyingness was used for outlier analysis. Partial least squares discriminant analysis (PLSDA) was then used to classify the grades of tea based on NIR spectra. Different data preprocessing methods, including smoothing, taking second-order derivative (D2) spectra, and standard normal variate (SNV) transformation, were performed to reduce unwanted spectral variations in samples of the same grade before classification models were developed. The results demonstrate that smoothing, taking D2 spectra, and SNV can improve the performance of PLSDA models. With SNV spectra, the model sensitivity was 1.000, 0.955, and 0.924, and the model specificity was 0.979, 0.952, and 0.996 for samples of three grades, respectively. FT-NIR spectrometry and chemometrics can provide a robust and effective tool for rapid internal quality control of Longjing green tea.

Comparative whole-genome analysis reveals artificial selection effects on Ustilago esculenta genome

Abstract Ustilago esculenta, infects Zizania latifolia, and induced host stem swollen to be a popular vegetable called Jiaobai in China. It is the long-standing artificial selection that maximizes the occurrence of favourable Jiaobai, and thus maintaining the plant–fungi interaction and modulating the fungus evolving from plant pathogen to entophyte. In this study, whole genome of U. esculenta was sequenced and transcriptomes of the fungi and its host were analysed. The 20.2 Mb U. esculenta draft genome of 6,654 predicted genes including mating, primary metabolism, secreted proteins, shared a high similarity to related Smut fungi. But U. esculenta prefers RNA silencing not repeat-induced point in defence and has more introns per gene, indicating relatively slow evolution rate. The fungus also lacks some genes in amino acid biosynthesis pathway which were filled by up-regulated host genes and developed distinct amino acid response mechanism to balance the infection–resistance interaction. Besides, U. esculenta lost some surface sensors, important virulence factors and host range-related effectors to maintain the economic endophytic life. The elucidation of the U. esculenta genomic information as well as expression profiles can not only contribute to more comprehensive insights into the molecular mechanism underlying artificial selection but also into smut fungi–host interactions.

Investigation on the differentiation of two Ustilago esculenta strains - implications of a relationship with the host phenotypes appearing in the fields

BackgroundUstilago esculenta, a pathogenic basidiomycete fungus, infects Zizania latifolia to form edible galls named Jiaobai in China. The distinct growth conditions of U. esculenta induced Z. latifolia to form three different phenotypes, named male Jiaobai, grey Jiaobai and white Jiaobai. The aim of this study is to characterize the genetic and morphological differences that distinguish the two U. esculenta strains.ResultsIn this study, sexually compatible haploid sporidia UeT14/UeT55 from grey Jiaobai (T strains) and UeMT10/UeMT46 from white Jiaobai (MT strains) were isolated. Meanwhile, we successfully established mating and inoculation assays. Great differences were observed between the T and MT strains. First, the MT strains had a defect in development, including lower teliospore formation frequency and germination rate, a slower growth rate and a lower growth mass. Second, they differed in the assimilation of nitrogen sources in that the T strains preferred urea and the MT strains preferred arginine. In addition, the MT strains were more sensitive to external signals, including pH and oxidative stress. Third, the MT strains showed an infection defect, resulting in an endophytic life in the host. This was in accordance with multiple mutated pathogenic genes discovered in the MT strains by the non-synonymous mutation analysis of the genome re-sequencing data between the MT and T strains (GenBank accession numbers of the genome re-sequencing data: JTLW00000000 for MT strains and SRR5889164 for T strains).ConclusionThe MT strains appeared to have defects in growth and infection and were more sensitive to external signals compared to the T strains. They displayed an absolutely stable endophytic life in the host without an infection cycle. Accordingly, they had multiple gene mutations occurring, especially in pathogenicity. In contrast, the T strains, as phytopathogens, had a complete survival life cycle, in which the formation of teliospores is important for adaption and infection, leading to the appearance of the grey phenotype. Further studies elucidating the molecular differences between the U. esculenta strains causing differential host phenotypes will help to improve the production and formation of edible white galls.

Analysis of Antioxidant Activity of Chinese Brown Rice by Fourier-Transformed Near Infrared Spectroscopy and Chemometrics

This paper develops a rapid method using near infrared (NIR) spectroscopy for analyzing the antioxidant activity of brown rice as total phenol content (TPC) and radical scavenging activity by DPPH (2,2-diphenyl-2-picrylhydrazyl) expressed as gallic acid equivalent (GAE). Brown rice () collected from five producing areas was analyzed for TPC and DPPH by reference methods. The NIR reflectance spectra were measured with compact powders of samples and no treatment was used. Full-spectrum partial least squares (FS-PLS) and interval PLS (iPLS) were used as the regression methods to relate the antioxidant activity values to the NIR data. The spectral range of 4800–5600 cm−1 plus 6000–6400 cm−1 has the best correlation with TPC, while the range of 4400–5200 cm−1 plus 6000–6400 cm−1 is the most suitable for predicting DPPH. With standard normal variate (SNV) transformation and the selected wavelength ranges, the root mean squared error of prediction (RMSEP) is 0.062 mg GAE g−1 for TPC and 0.141 mg GAE g−1 for DPPH radical, respectively. The multiple correlation coefficients of predictions for TPC and DPPH are 0.962 and 0.974, respectively. The developed NIR method might have a potential application to quality control of brown rice in the domestic market.

Automatic and rapid discrimination of cotton genotypes by near infrared spectroscopy and chemometrics.

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n = 120) and leaves (n = 123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.