Jiyong Shi

Determination of total flavonoids content in fresh Ginkgo biloba leaf with different colors using near infrared spectroscopy

Total flavonoids content is often considered an important quality index of Ginkgo biloba leaf. The feasibility of using near infrared (NIR) spectra at the wavelength range of 10,000-4000cm(-1) for rapid and nondestructive determination of total flavonoids content in G. biloba leaf was investigated. 120 fresh G. biloba leaves in different colors (green, green-yellowish and yellow) were used to spectra acquisition and total flavonoids determination. Partial least squares (PLS), interval partial least squares (iPLS) and synergy interval partial least squares (SiPLS) were used to develop calibration models for total flavonoids content in two colors leaves (green-yellowish and yellow) and three colors leaves (green, green-yellowish and yellow), respectively. The level of total flavonoids content for green, green-yellowish and yellow leaves was in an increasing order. Two characteristic wavelength regions (5840-6090cm(-1) and 6620-6880cm(-1)), which corresponded to the absorptions of two aromatic rings in basic flavonoid structure, were selected by SiPLS. The optimal SiPLS model for total flavonoids content in the two colors leaves (r(2)=0.82, RMSEP=2.62mg g(-1)) had better performance than PLS and iPLS models. It could be concluded that NIR spectroscopy has significant potential in the nondestructive determination of total flavonoids content in fresh G. biloba leaf.

Determination of pork spoilage by colorimetric gas sensor array based on natural pigments

A new colorimetric gas-sensor array based on four natural pigments, that were extracted from spinach (Spinacia oleracea), red radish (Raphanus sativus L.), winter jasmine (Jasminum nudiflorum), and black rice (Oryza sativa L. indica), was developed for pork freshness evaluation. A colour change profile for each sample was obtained by differentiating the images of the sensor array before and after exposure to the odour of sample. The total viable count (TVC) per gram of pork was obtained by classical microbiological plating methods, and the biogenic amines were measured by HPLC. Biogenic amine index (BAI) for the determination of meat freshness was developed from the sum of putrescine and cadaverine. The colour change profiles were analysed using principal component analysis and correlated with conventional methods (BAI, TVC). A partial least squares (PLS) prediction model was obtained with r=0.854 and 0.933 for BAI and TVC, respectively.

Sensing the quality parameters of Chinese traditional Yao-meat by using a colorimetric sensor combined with genetic algorithm partial least squares regression

Yao-meat is a traditional Chinese salted meat. Total volatile basic nitrogen content (TVB-N), total viable bacterial count (TVC), and residual nitrite (RN) level are important indexes of freshness for Yao-meat. This paper attempted the feasibility to determine TVB-N content, TVC and RN level in Yao-meat by a colorimetric sensor array chip. A color change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to Yao-meats volatile organic compounds. Genetic algorithm partial least squares regression (GA-PLS) was proposed to build the relationship between the TVB-N content, TVC, RN and the color change profiles of sensor array, and to select informative chemically responsive dyes for the three quality parameters. The GA-PLS models were achieved with RTVB-N=0.812, RTVC=0.856, RRN=0.855, in prediction set. This study demonstrated that colorimetric sensory array with GA-PLS algorithm could be used successfully to analyze the quality of Chinese traditional Yao-meat.

Electrodeposition of gold nanoparticles and reduced graphene oxide on an electrode for fast and sensitive determination of methylmercury in fish

Fish consumption is the main source of methylmercury (CH3Hg+) exposure for humans. In this study, gold nanoparticles and reduced graphene oxide (AuNPs-RGO) modified electrode was fabricated for determination of CH3Hg+ in fish. The AuNPs-RGO composite was synthesized by electroreduction method. The composite was characterized by scanning electron microscopy and energy dispersive spectra, X-ray diffraction and Raman spectroscopy. Electrochemical performance of the proposed sensor was studied by cyclic voltammetry, electrochemical impedance spectroscopy and differential pulse stripping voltammetry. The excellent conductivity and large surface of graphene contributed to an improvement in the voltammetric stripping signal. Under the optimized conditions, the methylmercury concentration in the range from 3 to 24μgL-1 had a good linear relation with the peak current. The detection limit of AuNPs-RGO modified electrode was 0.12μgL-1. Finally, the developed electrode was applied to detect methylmercury in fish samples, and the obtained results were in good agreement with certified values.

Microfabricated interdigitated Au electrode for voltammetric determination of lead and cadmium in Chinese mitten crab (Eriocheir sinensis).

An in-situ plating bismuth modified interdigitated Au electrode (IAE) was developed for determination of Pb(2+) and Cd(2+) in crab. The IAE was fabricated and used as counter electrode and working electrode. Cyclic voltammetry (CV) was performed with the IAE for studying electrochemical performance, showing clear redox peaks. The key operational parameters were optimized, which were 600 μg L(-1) of Bi(3+) concentration, 0.1 mol L(-1) of acetate buffer (pH 4.5), -1.2V of deposition potential and 180 s of deposition time. Under optimized condition, the linear range of IAE was from 5 to 50 μg L(-1) for both metal ions, with detection limit (threefold signal-to-noise) of 0.74 μg L(-1) for Pb(2+) and 0.86 μg L(-1) for Cd(2+). Finally, the developed sensor was applied to detect Pb(2+) and Cd(2+) in crab extract solutions by standard addition method. The results were in good agreement with outcomes obtained by inductively coupled plasma mass spectrometry.

Near infrared quantitative analysis of total flavonoid content in fresh Ginkgo biloba leaves based on different wavelength region selection methods and partial least squares regression

Total flavonoid concentration is often considered an important quality attribute of Ginkgo biloba leaf. Near infrared spectroscopy was used to determine total flavonoid concentration in fresh G. biloba leaf. The spectra of 120 leaf samples were acquired in the wavelength range of 10,000 cm−1 to 4000 cm−1. After pre-processing, interval partial least squares (iPLS), synergy interval partial least squares (SiPLS), genetic algorithm interval partial least squares (GA-iPLS) and simulation annealing algorithm interval partial least squares (SAA-iPLS) were used to select the most informative wavelength regions correlated with total flavonoid concentration. The number of wavelength regions and the number of PLS factors were optimised by cross-validation. The performance of the SAA-iPLS model developed in this study was better than PLS, iPLS and GA-iPLS models. The coefficient of determination (r2) and the root mean square error of prediction (RMSEP) for the prediction set samples using the SAA-iPLS model were 0.89 mg g−1 and 3.0 mg g−1, respectively. These results show that near infrared spectroscopy combined with SAA-iPLS has significant potential for the non-destructive quantitative analysis of total flavonoids in G. biloba leaf.

A rapid and nondestructive method to determine the distribution map of protein, carbohydrate and sialic acid on Edible bird’s nest by hyper-spectral imaging and chemometrics

Edible birds nest (EBN) is a precious functional food in Southeast Asia. A rapid and nondestructive method for determining the distribution map of protein content (PC), carbohydrate content (CC) and sialic acid content (SAC) on EBN sample was proposed. Firstly, 60 EBNs were used for hyperspectral image acquisition, and components content (PC, CC and SAC) were determined by chemical analytical methods. Secondly, the spectral signals of EBN hyperspectral image and EBN components content were used to build calibration models. Thirdly, spectra of each pixel in EBN hyperspectral image were extracted, and these spectra were substituted in the calibration models to predict the PC, CC and SAC of each pixel in the EBN image, so the visual distribution maps of PC, CC and SAC on the whole EBN were obtained. It is the first time to show the distribution tendency of PC, CC and SAC on the whole EBN sample.

A heuristic and parallel simulated annealing algorithm for variable selection in near‐infrared spectroscopy analysis

A new heuristic and parallel simulated annealing algorithm was proposed for variable selection in near‐infrared spectroscopy analysis. The algorithm employs a parallel mechanism to enhance the search efficiency, a heuristic mechanism to generate high‐quality candidate solutions, and the concept of Metropolis criterion to estimate accuracy of the candidate solutions. Several near‐infrared datasets have been evaluated under the proposed new algorithm, with partial least squares leading to improved analytical figures of merit upon wavelength selection. Improved robust and predictive regression models were obtained by the new algorithm. The method could also be helpful in other chemometric activities such as classification or quantitative structure‐activity relationship problems.

Determination of Retrogradation Degree in Starch by Mid-infrared and Raman Spectroscopy during Storage

Retrogradation behavior is an important physicochemical property of starch during storage. A fast and sensitive method was developed for determining the retrogradation degree (RD) in corn starch by mid-infrared (MIR), Raman spectroscopy, and combination of MIR and Raman. MIR and Raman spectra were collected from different retrogradation starch and then processed by partial least squares (PLS), interval PLS (iPLS), synergy interval PLS (siPLS), and backward interval PLS (biPLS). Two different levels of fusion data extracted from MIR and Raman spectra were analyzed by PLS. The developed models demonstrated that both MIR and Raman techniques combined with chemometrics can be used to determine the RD in starch. The PLS model built by medium-level fusion approach achieved the most satisfied performance with a correlation coefficient of 0.9658. Integrating MIR and Raman technique combined with chemometrics improved the prediction performance of RD in comparison with a single technique.

Pre-visual diagnostics of phosphorus deficiency in mini-cucumber plants using near-infrared reflectance spectroscopy.

The morphological symptoms of phosphorus (P) deficiency in the leaves of mini-cucumber plants at early stages of development have features similar to that of early stage development in healthy plants. That similarity may lead to inappropriate visual diagnostics of phosphorus deficiency in analyzed samples. Because the differences in spectral properties of leaf tissues between phosphorus-deficient and healthy plants can be demonstrated, the feasibility of using near-infrared (NIR) spectroscopy for rapid and nondestructive diagnostics of phosphorus deficiency in mini-cucumber plants was investigated. Leaf reflection spectra in the wavelength range of 10 000-4000 cm−1 were measured before the appearance of morphological changes caused by phosphorus deficiency. Least-squares support vector machine (LS-SVM), a method for recognizing patterns, was applied to identify phosphorus-deficient plants. Parameters (γ, σ 2 ) of LS-SVM were optimized by cross-validation, and several conventional, two-class classification methods such as linear discrimination analysis and K-nearest neighbors were also used comparatively for identification. Identification rates in excess of 86% were achieved with the LS-SVM model for both the training set and the prediction set. The overall results indicated that NIR spectra combined with LS-SVM could be used efficiently for pre-visual diagnostics of phosphorus deficiency in mini-cucumber plants.