Ying Zhou

Application of NIR spectroscopy for firmness evaluation of peaches

The use of near infrared (NIR) spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800∼2500 nm by InGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts (including three latitudes and three longitudes) were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes (i, ii, iii) and latitudes (A, B, C). For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression (PLSR) models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction (MSC) pretreatment, with correlation coefficient of cross-validation rcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of calibration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.

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 (800∼2500 nm), short NIR (800∼1100 nm), and long NIR (1100∼2500 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 °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.

NIR assessment of soluble solids and firmness for pears of different cultivars

Development of nondestructive measurements of soluble solids and firmness, which are two important ripeness and quality attributes of fruits, benefits the producers, processors and packers. The objective of this research was to evaluate the use of near-infrared (NIR) spectroscopy in detecting soluble solid content (SSC) and firmness for pears of three cultivars Cuiguan, Xueqing and Xizilv (n=160 of each cultivar). Relationships between nondestructive NIR spectral measurements and firmness and SSC of pear fruits were established by partial least square regression (PLSR) method. Models were developed for each cultivar, every two cultivars, and for all three cultivars in the spectral range of 800-2500 nm. The results of the models for all three cultivars turned out the best. For SSC assessment: correlation coefficients of calibration (rcal), root mean standard errors of calibration (RMSEC) and root mean standard errors of prediction (RMSEP) were 0.93, 0.35 °Brix and 0.50 °Brix for all three cultivars, respectively. For firmness assessment: rcal, RMSEC and RMSEP were0.92, 2.29 N, 2.95 N for all three cultivars, respectively. The results indicate that NIR spectroscopy can be used for predicting SSC and firmness of pear fruit and are the basis for the development of NIR analyzer suitable for on line application.

Near-infrared transmittance spectroscopy for nondestructive determination of soluble solids content and pH in tomato juice

The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids contents (SSC) and pH in tomato juices was investigated. A total of 200 tomato juice samples were used for NIR spectroscopy analysis at 800-2400 nm using FT-NIR spectrometer. Multiplicative signal correcton (MSC), the first and second derivative were applied for preprocessing spectral data. The relationship between SSC, pH and FT-NIR spectra of tomato juice was analyzed via partial least-squares (PLS) regression, respectively. PLS regression models for SSC and pH in tomato juices show the high accuracy. The correlation coefficient (r), root mean square error of calibration (RMSEC) and root mean square error of validation (RMSEP), root mean square error of cross-validation (RMSECV) for SSC were 0.91582, 0.0703, 0.150 and 0.138, respectively, whereas those values for pH were 0.8997, 0.0333, 0.0316 and 0.0489, respectively. It is concluded that the NIR transmittance spectroscopy is promising for the fast and nondestructive detection of chemical components in tomato juices.

Study on the oxidation process of tomato juice during storage by near-infrared spectroscopy

Near-infrared (NIR) transmittance spectroscopy combined with several chemometrical techniques was investigated to study the oxidation process during storage in tomato juices. A total of 100 tomato juice samples were used for NIR spectroscopy analysis at 800-2400 nm using FT-NIR spectrometer. The spectrum of each tomato juice was collected twice: the first time as soon as the tomatoes were squeezed, centrifuged, filtered and the tomato juice had not undergone any oxidation process and the second measurement was taken after a month. Principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were applied to discriminate between the two groups of spectra. The results show that differences between tomato juices before and after the storage period do exist attributed to changes in certain compounds of juice and excellent classification can be obtained after optimizing spectral pretreatment.

Influence of humidity on spectral performance for near-infrared detection of fruit

Spectral performance would be affected by many factors such as temperature, equipment parameters and so on. Humidity fluctuations may occur in practice because of varying weather conditions. The objective of this research was to find out whether the change of humidity would influence the near infrared spectrum of samples. In this trial, an airproof, humidity-controllable test-bed was established to change the humidity of the mini environment. At 40%, 50%, 60%, 70% and 80% degrees of humidity, each samples final spectrum was attained by removing the backgrounds spectrum from the samples. For whether the influence of the samples and the backgrounds spectrum are equal was not known, This trial was divided into two groups: detecting background and sample at each degree of humidity (group 1) and backgrounds detecting just happened at 40% degree of humidity (group 2). This research was based on the hardware of NEXUS intelligent FT-IR spectrometer, made by Nicolet instrument company U.S.A, with using fiber optic diffuse reflectance accessory. The final spectrum was analysed using single variance analysis and Mahalanobis Distance methods. The result shows that neither in group 1 nor 2, humidity had little influence on NIR.

Discrimination of planting area of white peach based near-infrared spectra and chemometrics methods

White peach is a famous peach variety for its super-quality and high economic benefit. It is originally planted in Yuandong Villiage, Jinhua County, Zhejiang province. By now, it has been planted in many other places in southeast of China. However, peaches from different planting areas have dissimilar quality and taste, which result in different selling price. The objective of this research was to discriminate peaches from different planting areas by using near-infrared (NIR) spectra and chemometrics methods. Diffuse reflectance spectra were collected by a fiber spectrometer in the range of 800-2500 nm. Discriminant analysis (DA), soft independent modeling of class analogy (SIMCA), and discriminant partial least square regression (DPLS) methods were employed to classify the peaches from three planting areas Jinhua, Wuyi, and Yongkang of Zhejiang province. 360 samples were used in this study, 120 samples per planting area. The classifying correctness were above 92% for both DA and SIMCA mdoels. And the result of DPLS model was slightly better. By using DPLS method, two Jinhua peaches, three Wuyi peaches, and three Yongkang peaches were misclassified, the accruacy was above 95%. The results of this study indicate that the three chemometrics methods DA, SIMCA, and DPLS are effective for discriminating peaches from different planting areas based on NIR spectroscopy.

Determination of Chinese rice wine from different wineries by near-infrared spectroscopy combined with chemometrics methods

In this paper, 104 samples of Chinese rice wines of the same variety (Shaoxing rice wine), collected in three winery (guyuelongshan, pagoda brand, kuaijishan), three brewed years (2002, 2004, 2004-2006) were analyzed by near-infrared transmission spectroscopy between 800 and 2500 nm. The spectral differences were studied by principal components analysis (PCA), and Classifications, according the brand, were carried out by discriminant analysis (DA) and partial least squares discriminant analysis (PLSDA). The DA model gained a total accuracy of 94.23% and when used to predict the brand of the validation set samples, a better result, correctly classified all of the three kinds of Chinese rice wine up to 100%, are obtained by PLSDA model. The work reported here is a feasibility study and requires further development with considerable samples of more different brands. Further studies are needed in order to improve the accuracy and robustness, and to extend the discrimination to other Chinese rice wine varieties or brands.

Evaluation of goose down and duck’s down’s content by NIR method

Down products have large market share, especially in China, but it’s hard to distinguish goose down and duck down by visual appraisal. The traditional method is using microscope to attain the goal. But there are some problems such as limited samples and low accuracy. The objective of this study was to discriminate goose down and duck down with different pureness using Fourier transform (FT) near infrared (NIR) spectroscopy. In this study, 4 groups of goose down and duck down with different proportion were prepared. Group 1 and group 2 are made up of pure goose down and pure duck down, separately. Group 3 is made up of 1/3 goose down and 2/3 duck down, and group 4 is made up of 2/3 goose down and 1/3 duck down. NIR instrument was used to scan every sample in order to acquire spectrum. This research was based on the hardware of NEXUS intelligent FT-IR spectrometer, with using fiber optic diffuse reflectance accessory. Spectrum of each sample was acquired using diffuse reflectance mode. Chemometrics methods including PCA and DPLS were used to analyse the spectrum attained. Various pre-treatment of experimental data such as MSC and derivative were also used to make better model. The result shows that NIR is a fast and effective method to discriminate between goose down and duck down even in mixture.

Estimating Trace Metals in Chinese Rice Wine by Near Infrared Spectroscopy

To evaluate the applicability of near infrared (NIR) spectroscopy for predicting nconcentration of trace metals (potassium, calcium, sodium, zinc and iron) in Chinese rice wine, ntransmission spectra were collected in the spectral range from 800 nm to 2500 nm in a 5 mm npath-length rectangular quartz cuvette with air as reference at room temperature. The 28 nsamples were of different wine age (1 year, 3 years and 5 years). The content of trace metals n(potassium, magnesium, zincum, and iron) was determined by atomic absorption spectroscopy n(AAS). Five calibration equations for the reference parameters were established between the nreference data and NIR spectra by partial least squares (PLS) regression. The results for npotassium, calcium, magnesium were promising. The determination coefficients of validation (R2 nval) for the three trace metals were 0.81, 0.79, and 0.88, and root mean square error of nprediction (RMSEP) were 17.40 mg L-1, 24.80 mg L-1, and 2.20 mg L-1, respectively. However, nthe results for zincum, and iron were not good, R2 nval were 0.51 and 0.52, respectively.. The npreliminary results indicated that NIR spectroscopic technique could offer screening capability for npotassium, calcium, magnesium in Chinese rice wine, and warranted further research using a nlarger number of Chinese rice wines over a wider range of wine age.