[Evaluation of Sugar Content of Huanghua Pear on Trees by Visible/Near Infrared Spectroscopy].

A method of ambient light correction was proposed to evaluate the sugar content of Huanghua pears on tree by visible/near infrared diffuse reflectance spectroscopy (Vis/NIRS). Due to strong interference of ambient light, it was difficult to collect the efficient spectral of pears on tree. In the field, covering the fruits with a bag blocking ambient light can get better results, but the efficiency is fairly low, the instrument corrections of dark and reference spectra may help to reduce the error of the model, however, the interference of the ambient light cannot be eliminated effectively. In order to reduce the effect of ambient light, a shutter was attached to the front of probe. When opening shutter, the spot spectrum were obtained, on which instrument light and ambient light acted at the same time. While closing shutter, background spectra were obtained, on which only ambient light acted, then the ambient light spectra was subtracted from spot spectra. Prediction models were built using data on tree (before and after ambient light correction) and after harvesting by partial least square (PLS). The results of the correlation coefficient (R) are 0.1, 0.69, 0.924; the root mean square error of prediction (SEP) are 0. 89°Brix, 0.42°Brix, 0.27°Brix; ratio of standard deviation (SD) to SEP (RPD) are 0.79, 1.69, 2.58, respectively. The results indicate that, method of background correction used in the experiment can reduce the effect of ambient lighting on spectral acquisition of Huanghua pears in field, efficiently. This method can be used to collect the visible/near infrared spectrum of fruits in field, and may give full play to visible/near-infrared spectroscopy in preharvest management and maturity testing of fruits in the field.

[Application of hyperspectral fluorescence image technology in detection of early rotten oranges].

Rottenness is most prevalent and devastating disease that threats citrus fruit. Automatic detection of early rottenness can enhance the competitiveness and profitability of the citrus industry. However, there is no efficient automatic detection technology at this time that could detect this disease. The navel orange was selected as research objective. Hyperspectral fluorescence imaging was used to detect early rottenness in orange. Optimum index factor (OIF) method was applied to identify the optimal band combination. 100% detection rate was achieved based on the optimal bands ratio image and two threshold values. The research showed that the proposed method can effectively overcome the affect from florescence effect because stem damage area and stem also can produce florescence under ultraviolet light. This study will lay a foundation for developing multispectral detection system used in on-line detection of early rottenness fruit.

[Quantitative analysis of goose and duck mixed down using visible/NIR spectroscopy].

Goose down and duck down have very similar appearance but the quality of goose down is better than that of duck down in general. There is a highest allowable limit as specified by the various national standards of feather and down for the percentage of duck feather or down mixed in goose feather or down. Traditional detection method, manual inspection with a high-scale microscope, is labor intensive and not suitable for large-volume samples analysis and on-site rapid testing. In the present paper, visible/near-infrared (NIR) spectroscopy combined with successive projection algorithm (SPA) for characteristic wavelengths selection was used to determinate the content of duck down mixed in goose down. In the range of 450-930 nm, the multiple linear regression (MLR) model established with the 8 characteristic wavelengths selected by SPA achieved good prediction, the correlation coefficient of 0.983, root mean square error of calibration (RMSEC) of 5.44%, and root mean square error of prediction (RMSEP) of 5.75%. Therefore, it is expected to be used for rapid detection of feather and down quality in future.

[Advance on application of hyperspectral imaging to nondestructive detection of agricultural products external quality].

Hyperspectral imaging is an emerging technique that integrates conventional imaging and spectroscopy to obtain both spatial and spectral information from a studied object simultaneously. The images data can reflect the external features, surface defects and contamination. The spectra data can analyze physical structure and chemical composition in studied object. Therefore, hyperspectral imaging technology causes more and more attention, and has become a hot research topic recently. In order to track the latest research developments at home and abroad, application of hyperspectral reflectance and fluorescence imaging techniques to nondestructive detection of agricultural products external quality was reviewed, which would provide reference for application of hyperspectral imaging to agriculture.

[Rapid detection of Escherichia coli O157 : H7 using surface plasmon resonance (SPR) biosensor].

A biosensor based on surface plasmon resonance (SPR) was developed for rapid detection of Escherichia coli O157 : H7. BIACORE 3000 SPR instrument and a dextran-modified sensor chip (CM5) were used. After activation with EDC/NHS, anti-E. coli O157 : H7 antibody was immobilized on the gold surface of the SPR sensor, and then following ethanolamine was injected, and the chip was ready for E. coli O157 : H7. Regeneration was achieved using NaOH in order to detect several samples. The limit of detection was found to be 3 x 10(5) CFU x mL(-1) for E. coli O157 : H7, and the change of RU was linearly correlated with the concentration of E. coli O157 : H7 cells (R2 = 0.99). The detection time ranged from 5 to 7 min, and the result of regeneration was effective which allowed the chip to be reused for more than 50 samples. This method is convenient, and stable, and shows potentials for applications in food areas.

[Applications of atomic emission spectrum from liquid electrode discharge to metal ion detection].

The fast and precise detection of metal ion is an important research project concerning studies in diverse academic fields and different kinds of detecting technologies. In the present paper, the authors review the research on atomic emission spectrum based on liquid electrode discharge and its applications in the detection of metal ion. In the first part of this paper the principles and characteristics of the methods based on electrochemistry and spectroscopy were introduced. The methods of ion-selective electrode (ISE), anodic stripping voltammetry, atomic emission spectrum and atomic absorption spectrum were included in this part and discussed comparatively. Then the principles and characteristics of liquid electrode spectra for metal ion detection were introduced. The mechanism of the plasma production and the characteristics of the plasma spectrum as well as its advantages compared with other methods were discussed. Secondly, the authors divided the discharge system into two types and named them single liquid-electrode discharge and double-liquid electrode respectively, according to the number of the liquid electrode and the configuration of the discharge system, and the development as well as the present research status of each type was illustrated. Then the characteristics and configurations of the discharge systems including ECGD, SCGD, LS-APGD and capillary discharge were discussed in detail as examples of the two types. By taking advantage of the technology of atomic emission spectrum based on liquid electrode discharge, the detecting limit of heavy metals such as copper, mercury and argent as well as active metal ions including sodium, potass and magnesium can achieve microg x L(-1). Finally, the advantages and problems of the liquid-electrode discharge applied in detection of metal ion were discussed. And the applications of the atomic emission spectrum based on liquid electrode discharge were prospected.

[Research on the trash content measurement and classification of ginned cotton by using NIR spectroscopy technique].

Near infrared (NIR) spectroscopy was investigated to predict trash content and classify types of ginned cotton by using a fiberoptic in diffuse reflectance mode. Different spectra preprocessing methods were compared, and partial least-squares (PLS) regression was established to predict the trash content of ginned cotton. Discriminant analysis (DA) was used to classify various types of lint and content level of trash. The correlation coefficient r was 0.906 for optimal PLS model using three factors based on first-order derivative spectra, and RMSEC and RMSEP was 0.440 and 0.823 respectively. To classify ginned cotton with and without plant trash, the accuracy rate reached 95.4% using 15 principal components (PCs) via DA, whereas the prediction accuracy rate was only 80.9% for the classification of sample types due to containing foreign fiber, and the classification result for the content level of trash in lint was not good for the samples without any preprocessing. The result indicated that the NIR spectra of sample can be used to predict trash content in ginned cotton, which is often disturbed by type, content and distribution of foreign matters, and the accuracy of some prediction model is unsatisfactory. In order to improve the prediction accuracy, some methods would be applied in future research, such as pretreatment according to acquisition request of solid sample, or using transmission mode.

On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy.

The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (R(c))=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction=0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.

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 (800approximately2500 nm), short NIR (800approximately1100 nm), and long NIR (1100approximately2500 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 degrees 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.

[Applications of terahertz spectroscopy and imaging techniques in food safety inspection].

With the development of ultra-fast laser technology and the further understanding of the mechanism behind the interaction of terahertz radiation and materials, THz radiation is actively developed as a new-style technology for a wide range of applications. Currently THz spectroscopy and imaging techniques show great promise in security detection, medical diagnosis, wireless communication, pharmaceutical and many other areas. Because of the growing importance of food safety issues, the detection technology of food safety is also faced with great opportunities and challenges, while THz spectroscopy and imaging techniques provide a new solution to the problem. Compared to other technologies, THz radiation with higher signal-to-noise ratio and wide dynamic range can simultaneously obtain both frequency-domain and time-domain information of the sample which relates to the information of physical structure and chemical composition of materials. Above all THz radiation features unique advantages in food safety inspection. In the present paper, the brief concept of terahertz radiation was introduced, while the properties and technical methods of THz wave were also discussed. The most recent progress in THz technology used in food safety inspection was summarized. The prospect and restricted factors of this novel technology in food industry were also discussed.

[Application and recent development of research on near-infrared spectroscopy for meat quality evaluation].

As one of new optical detection methods, near-infrared spectroscopy (NIRS) technique has been widely used in food industry in recent years. NIRS technique is also promising for quality evaluation of meat and meat products and is characterized by its quickness, online operation and nondestructive detection. The present paper reviews the main application and recent development of research on near-infrared spectroscopy in meat industry, including components analysis, sensory quality evaluation as well as discrimination of production. It's necessary to determine the content of main chemical components in a variety of meat such as protein, fat, water etc as they exert important influence on meat quality. Sensory quality including tenderness, water holding capacity, color, and freshness is commonly evaluated by human sensory system. Thus there is an obvious potential profit to achieve online determination industrialization for meat quality. Additionally the utilization of NIRS in quality detection of common meat products is particularized in this paper. Most study of near-infrared spectroscopy technique for meat quality evaluation lays emphasis on component analysis that especially has shown a progress in the determination of protein, fat, water and part of fatty acid, which has been investigated much recently. Not any kind of sensory quality can be well predicted by NIRS as it depends on the species of meat and the limitation of this optical technique. Therein beef is the mostly used object with many reports on the evaluation of tenderness compared to other types. There is a lot of investigation for sensory quality detection of pork on water holding capacity etc. Meanwhile this review also tries to come up with some perspectives on meat quality detection with near-infrared spectroscopy according to current development trend: on the basis of deeply improving the meat detection precision, near-infrared spectroscopy technique combined with other non-detection techniques like machine vision will be investigated in order to realize overall evaluation of meat quality.

[Progress in application of near infrared spectroscopy to nondestructive on-line detection of products/food quality].

Increasing safety events caused by products/food quality problems has caught more and more attention, therefore non-destructive fast detection of products/food quality becomes necessary. At present, fast detection methods commonly used include chemical colorimetric method, near infrared spectroscopy (NIRS) technique, immunoassay method, bio-sensor technique, biomicroarray method, bioluminescence method, and so on. NIRS technique has found wide application in products/food quality detection because of its characteristics such as very high speed, no sample preparation, non-destruction, no pollution, low cost etc. Many research works have been done on products/food quality detection using NIRS technique both home and abroad, from static laboratory investigations to online investigations. In the present paper, basic knowledge of NIRS and its analysis process were briefly introduced. The applications of NIRS technique in online quality detection and control of fruit, fish, meat, milk, grain, fermentation of cheese and alcohol etc were reviewed. Finally, the existing problems of NIRS were pointed out and the prospect of NIRS technique was discussed. In the future, NIRS technique will combine with network technique to realize online update and upgrade of NIR models. And spectral imaging technique will be the development trend of NIRS technique in the 21st century.

[Qualitative analysis of fragrant pear class based on near infrared diffuse reflectance spectroscopy].

A method was developed to automatically discriminate the persistent calyx fruit and fruit without calyx of fragrant pear by means of near infrared spectroscopy (NIRS). The prediction performance of different band regions range, different principal component numbers and different preprocessing methods of the spectra (multiplicative signal correction, standard normal variate, and derivative spectra) together with discriminant analysis (DA) was also investigated, and The calibration model was established to classify the different kinds of fragrant pear. The research results for the fragrant pear classification showed that DA calibration models using these parameters with band regions between 9 091 and 4 000 cm(-1) and original spectra are optimal, with the percentage of correct sample classification being 100% and 95% for the calibration and validation set, respectively.

[Application of near-infrared spectroscopy to detection of pesticide phoxim residues].

Near-infrared (NIR) spectroscopy technique was applied directly to the detection of pesticide phoxim residues. A sample pretreatment method was introduced. Samples were mixed with silica gel. Silica gel as a sorbent was employed to extract and enrich the low-concentration samples. Subsequently, diffuse reflection spectrum was measured on silica gel. Calibration models were developed using partial least square regression (PLSR) algorithm. Leave-one-out cross-validation was used to evaluate and compare the models. Two experiments were carried out, and the results show that 21 samples with the concentration gradient of 0.5 mg x L(-1) exhibited a high correlation coefficient of cross-validation of 0. 958, and a root mean square error of cross validation (RMSECV) of 0.872 mg x L(-1), while 41 samples with the concentration gradient of 0.25 mg x L(-1) gave a correlation coefficient of cross-validation of 0.924 and RMSECV of 1.15 mg x L(-1). It is indicated that with the reduction in concentration gradient, the prediction capacity of models dropped, but there still existed a high correlation coefficient with the concentration of phoxim in the samples. The experiments proved that the sample pretreatment method with the introduction of silica gel as an absorber to enrich low concentration analyte was effective. The method was able to lower the detection limit of NIR. The developed technique has a potential application in low-concentration sample detection by NIR spectroscopy, such as pesticide residues.

[Application of immunomagnetic separation to E. coli O157 : H7 detection].

In the present paper, the capture efficiencies of four different proportions of E. coli O157 : H7 and immunomagnetic beads (IMB) (1 : 6.34, 1 : 12.69, 1 : 25.38 and 1 : 30.07) and three different proportions of E. coli O157 : H7 and Bacillus subtilis (0.3 mL : 0.1 mL, 0.2 mL : 0.2 mL and 0.1 mL : 0.3 mL) were compared, with the total volume and capacity of IMB remaining constant. It was revealed that the capture efficiency could reach 100% when the proportion of E. coli O157 : H7 and IMB was 1 : 30, meaning that almost all targets E. coli O157 : H7 could be captured at 1 : 30, and that the capture efficiency decreased and then increased with the number of Bacillus subtilis increasing. Moreover, the combination of ATP bioluminescence and IMS was correlated well with conventional culture methods, R2 = 0.9882, and the detection limit was as low as 10(2) CFU x mL(-1).

[Identification of tea from different regions using X-ray fluorescence].

The existence of fake tea from non-origin seriously impacts on the credibility of the famous tea. A method was developed to identify tea from difference regions on the basis of the fact that the content of heavy metals in different origin tea is varied by using X-ray fluorescence technique and pattern recognition technique. Samples from different origins were grouped respectively, and their X-ray fluorescence spectra were acquired, and then the principal components of these spectral data were calculated, and the average of the principal components of each group was used as the center of each group. The Mahalanobis distance value between a sample and the center of a group were calculated, when the Mahalanobis distance value reached minimum, the sample was classed to current group, and in this way, a sample was identified. A Niton 792 portable X-ray spectrometer was used to class 120 tea samples from Anji, Jinhua, Hangzhou and Taizhou, in zhejiang province of China. It was found that the spectra between 3 and 13 KeV and the first 4 principal components give enough information for the identification of tea from different regions,and the rate of error was 4.2%.

[Studies on detection of E. coli O157:H7 by ATP bioluminescence].

In the present paper, a quantitative linear model betweena series of concentrations of E. coliO157 : H7 and counts by BPCL ultra weak luminescence analyzer was built up. And the influences of four different buffers with the same pH (pH = 7.4), Tris-HCl, PBS, KH2PO4-NaOH and Na2 HPO4-C6H8O7, and five different chemical substances with the same mass concentration (10 g x L), NaCl, KCl, NaOH, MgCl2 and NaH2PO4 on ATP bioluminescence were compared. The results showed that Tris-HCl was a suitable buffer for dilution, since it could distinguish well between different concentrations and had the lowest background signals. And MgCl2 could intensify luminescence distinctly, while the other four chemical substances decreased luminescence, of which NaOH decreased luminescence most obviously. Moreover, ATP bioluminescence was correlated well with conventional culture methods (r = 0.96), and the detection limit was 10(3) cells x mL(-1).

[NIR spectroscopy based on least square support vector machines for quality prediction of tomato juice].

The application of least square support vector machines (LS-SVM) regression method based on statistics study theory to the analysis with near infrared (NIR) spectra of tomato juice was introduced in the present paper. In this method, LS-SVM was used for establishing model of spectral analysis, and was applied to predict the sugar contents (SC) and available acid (VA) in tomato juice samples. NIR transmission spectra of tomato juice were measured in the spectral range of 800-2,500 nm using InGaAs detector. The radial basis function (RBF) was adopted as a kernel function of LS-SVM. Sixty seven tomato juice samples were used as calibration set, and thirty three samples were used as validation set. The results of the method for sugar contents (SC) and available acid (VA) prediction were: a high correlation coefficient of 0.9903 and 0.9675, and a low root mean square error of prediction (RMSEP) of 0.0056 degree Brix and 0.0245, respectively. And compared to PLS and PCR methods, the performance of the LSSVM method was better. The results indicated that it was possible to built statistic models to quantify some common components in tomato juice using near-infrared (NIR) spectroscopy and least square support vector machines (LS-SVM) regression method as a nonlinear multivariate calibration procedure, and LS-SVM could be a rapid and accurate method for juice components determination based on NIR spectra.

[Effect of pathlength variation on the NIR spectra for quality evaluation of chinese rice wine].

Near infrared (NIR) spectroscopy and chemometrics were applied to determine the effect of pathlength variation on the spectra of the Chinese rice wine and the consequences of the prediction precision of calibration models developed for measuring alcoholic degree, sugar content, and pH was investigated in the present research Samples were scanned in transmission mode using a commercial FT-NIR spectrometer and a demountable liquid cell for versatile path length liquid sampling. By comparing the results of performance between models based on different optical pathlength (0.5, 1, 1.5, 2, 2.5, and 3 mm), the best indicators of optical pathlength were identified. Based on the optimum pathlength, the models for alcoholic degree, sugar content and pH were established. The best optical pathlength for the alcoholic degree was 2 mm, using partial least squares regression (PLSR) model with the original spectra, correlation coefficient (r) was 0.942, root mean standard error of calibration (RMSEC) and root mean standard error of cross-validation (RMESCV) were 0.256 (%, (phi)) and 0.292 (%, (phi)), respectively; the best optical pathlength for the sugar content was 1 mm, using PLSR model with the original spectra, r was 0.945, and RMSEC and RMESCV were 0.125% and 0.149%, respectively; the best optical pathlength for the pH was 2 mm, using PLSR model with the original spectra, r was 0.947, and RMSEC and RMESCV were 0.018 and 0.039, respectively. This study showed that pathlength variation had influence on the performance of calibration models for Chinese rice wine, and a suitable pathlength could effectively improve detection accuracy.

[Advance in nondestructive detection of fruit internal quality based on hyperspectral imaging].

The present paper reviews the development in the field of hyperspectral imaging technology for nondestructive detection of fruit internal quality in recent years up to the year 2007. With the increasing maturity of hyperspectral imaging technology, decline of cost for its hardware and software, and improvement in hyperspectral image data processing algorithms, hyperspectral imaging technology for fruit quality nondestructive detection has become a hot research topic. In order to track the latest research developments at home and abroad, the fruit internal quality (maturity, firmness, soluble solid content, water content) detection with hyperspectral imaging was reviewed, which would provide reference for Chinese researchers.