Fourier transform near infrared reflectance (FT-NIR) spectroscopy has been used successfully to measure soluble
solids content (SSC) in citrus fruit. However, for practical implementation, the technique needs to be able to compensate
for fruit temperature fluctuations, as it was observed that the sample temperature affects the near infrared reflectance
spectrum in a non-linear way. Temperature fluctuations may occur in practice because of varying weather conditions or
improper conditioning of the fruit immediately after harvest. Two techniques were found well suited to control the
accuracy of the calibration models for soluble solids with respect to temperature fluctuations. The first, and most
practical one, consisted of developing a global robust calibration model to cover the temperature range expected in the
future. The second method involved the development of a range of temperature dedicated calibration models. The
drawback of the latter approach is that the required data collection is very large. The global temperature calibration
model avoids temperature-sensitive wavelengths for the calibration of SSC. Global temperature models are preferred
above dedicated temperature models because of the following shortcomings of the latter. For each temperature, a new
calibration model has to be made, which is time-consuming.
To evaluate the applicability of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining ethanol concentration of Chinese rice wine in square brown glass bottle, transmission spectra of 100 bottled Chinese rice wine samples were collected in the spectral range of 350-1200 nm. Statistical equations were established between the reference data and VIS-NIR spectra by partial least squares (PLS) regression method. Performance of three kinds of mathematical treatment of spectra (original spectra, first derivative spectra and second derivative spectra) were also discussed. The PLS models of original spectra turned out better results, with higher correlation coefficient in calibration (Rcal) of 0.89, lower root mean standard error of calibration (RMSEC) of 0.165, and lower root mean standard error of cross validation (RMSECV) of 0.179. Using original spectra, PLS models for ethanol concentration prediction were developed. The Rcal and the correlation coefficient in validation (Rval) were 0.928 and 0.875, respectively; and the RMSEC and the root mean standard error of validation (RMSEP) were 0.135 (%, v v-1) and 0.177 (%, v v-1), respectively. The results demonstrated that VIS-NIR spectroscopy could be used to predict ethanol concentration in bottled Chinese rice wine.
The feasibility of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining wine age (1, 2, 3, 4,
and 5 years) of Chinese rice wine was investigated. Samples of Chinese rice wine were analyzed in 600 mL square
brown glass bottles with side length of approximately 64 mm at room temperature. VIS-NIR spectra of 100 bottled
Chinese rice wine samples were collected in transmission mode in the wavelength range of 350-1200 nm by a fiber
spectrometer system. Discriminant models were developed based on discriminant analysis (DA) together with raw, first
and second derivative spectra. The concentration of alcoholic degree, total acid, and °Brix was determined to validate the
NIR results. The calibration result for raw spectra was better than that for first and second derivative spectra. The
percentage of samples correctly classified for raw spectra was 98%. For 1-, 2-, and 3-year-old sample groups, the sample
were all correctly classified, and for 4- and 5-year-old sample groups, the percentage of samples correctly classified was
92.9%, respectively. In validation analysis, the percentage of samples correctly classified was 100%. The results
demonstrated that VIS-NIR spectroscopic technique could be used as a non-invasive, rapid and reliable method for
predicting wine age of bottled Chinese rice wine.
Watermelon is a popular fruit in the world. Soluble solids content (SSC) is major characteristic used for assessing watermelon internal quality. This study was about a method for nondestructive internal quality detection of watermelons by means of visible/Near Infrared (Vis/NIR) diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer when the watermelon was in motion (1.4m/s) and in static state. Spectra data were analyzed by partial least squares (PLS) method. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration (RMSEC), root mean square errors of prediction (RMSEP) and correlation coefficient (r) between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models and the PLS method can provide good results. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon both in motion and in static state, and the predicted values were highly correlated with destructively measured values. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon internal quality in a nondestructive way.
Food safety and quality concern have become more and more significant in recent years. There is therefore an increasing
focus on new technologies that can be applied to food quality evaluation or safety inspection, either to simplify or speed
up the checking process, or to provide additional functionality. For example, the technique of near infrared (NIR)
spectroscopy has been used for the authentication of agricultural products and food samples. Terahertz (THz) radiation,
or THz wave, is electromagnetic wave lies between mid-infrared and microwave radiation. During the past decade, THz
waves have been used to characterize the electronic, vibrational and compositional properties of solid, liquid and gas
phase materials. The main two applications in which THz fields involved are THz spectroscopy and THz imaging.
Terahertz wave technology, as a new area of research, has shown its wide prospects in imaging, diagnosis, detection, and
monitoring, etc. Recently, THz technology has gained a lot of attention from biological spectral analysis to bio-medical
imaging due to its unique features compared with microwave and optical waves. In this paper, a brief review is given to
summarize the progress of THz techqiues in the field of food inspection. The properties of THz wave, its uniqueness in
sensing and imaging applications, and the prospect of this novel technology in food industry were discussed.
To evaluate the applicability of near infrared spectroscopy for determination of the five enological parameters (alcoholic degree, pH value, total acid and amino acid nitrogen, °Brix) of Chinese rice wine, transmission spectra were collected in the spectral range from 12500 to 3800 cm-1 in a 1 mm path length rectangular quartz cuvette with air as reference at room temperature. Five calibration equations for the five parameters were established between the reference data and spectra by partial least squares (PLS) regression, separately. The best calibration results were achieved for the determination of alcoholic degree and °Brix. The RPD (ration of the standard deviation of the samples to the SECV) values of the calibration for both alcoholic degree and °Brix were higher than 3 (4.30 and 7.94, respectively), which demonstrated the robustness and power of the calibration models. The determination coefficients (R2) for alcoholic degree and °Brix were 0.987 and 0.991, respectively. The performance of pH, total acid and amino acid nitrogen was not as good as that of alcoholic degree and °Brix. The RPD values for the three parameters were 1.48, 1.85 and 1.82, respectively, and R2 values were 0.964, 0.970 and 0.971, respectively. In validation step, R2 value of the five parameters are all higher than 0.7, especially for alcoholic degree and °Brix (0.968 and 0.956, respectively). The results demonstrated that NIR spectroscopy could be used to predict the concentration of the five enological parameters in Chinese rice wine.
The near infrared (NIR) method based on fibre-optic FT-NIR spectrometer was tested to determine soluble solids content (SSC) non-destructively in chufa (Eleocharis tuberose schult). A total of 240 chufas (120 of cv. 'Jinhua' and 120 of cv. 'Yongkang') sampled from eight positions in the different fields to increase variation in soluble solids content, were measured after 2-days storage and the measurements randomly assigned to a calibration data set and a prediction data set. Thus the calibration set and the prediction set represented exactly the same distribution. The calibration data set was used to select the wavelengths best correlated with Brix and different regression methods (partial least squares (PLS) regression and multiple linear regression (MLR)) that was applied to calculate the Brix value in the prediction data set. The most significant r (0.9056) was found with the first derivative of log (1/R) (where R reflectance), yielding standard error of calibration (SEC)=0.545 Brix, standard error of prediction (SEP)=0.632 Brix. Analysis of different methods performed on the actual and the predicted Brix showed PLS is better than MLR. This NIR method seems reliable for determining soluble solids contents of chufa non-destructively, and could prove useful for it.
Vitamin C is considered an important nutrition component of fruits, especially of kiwifruit. Traditional destructive method for vitamin C measurement is very complex and fussy. Near Infrared (NIR)spectroscopy is a promising technique for nondestructive measurement of fruit internal qualities, such as soluble solid content (SSC), valid acidity (VA). The objective of this research was to study the potential of NIR diffuse reflectance spectroscopy as a way for nondestructive measurement of vitamin C content in "Qinmei" kiwifruit. NIR spectral data were collected in the spectral range of
800-2500 nm with different combinations of resolution (4 cm-1, 16 cm-1 and 32 cm-1) and scan number (32, 64 and 128). Statistical models were developed using partial least square (PLS) method. The combination with resolution of 4 cm-1 and scan number of 64 gave the best result when all samples were used in calibration sample set. Then two spectral pretreatments multiplicative signal correction (MSC) and standard normal variate (SNV), and three kinds of mathematical treatment of original spectra, first derivative spectra and second derivative spectra were discussed. The PLS model of second derivative spectra using SNV pretreatment turned out better prediction results: correlation coefficient (r) of 0. 93, root mean square error of calibration (RMSEC) of 9.24 mg/100g and root mean square error of prediction (RMSEP) of 10.3 mg/100g. The results of this study showed that NIR diffuse reflectance spectroscopy could be used for kiwifruit vitamin C prediction. The higher the resolution, the better the results, but longer time will be taken, which may not be suitable for on-line use. Therefore, further research still needs to be done.
Near-infrared (NIR) spectroscopy has become a very popular technique for the non-invasive assessment of intact fruit. This work presents an application of a low-cost commercially available NIR spectrometer for the estimation of soluble solids content (SSC) of Chinese citrus. The configuration for the spectra acquisition was used (diffuse transmittance), using a custom-designed contact optical fiber probe. Samples of Chinese citrus in deferent orchard, collected over the 2005 harvest seasons, were analyzed for soluble solids content (Brix). Partial least squares calibration models, obtained from several preprocessing techniques (smoothing, multiplicative signal correction, standard normal variate, etc), were compared. Also, the short-wave (SW-NIR) spectral regions were used. Performance of different models was assessed in terms of root mean square of cross-validation, root mean square of prediction (RMSEP) and R for a validation set of samples. RMSEP of 0.538 with R = 0.896 indicate that it is possible to estimate Chinese citrus SSC (Brix value), by using a portable spectrometer.
Chufa (Eleocharis tuberose Schult) is a special local product in south China. It is both vegetable and fruit. Near infrared spectroscopy was widely used for fruit and vegetable quality evaluation. The objective of this research was to study whether Chufa MT-firmness can be nondestructively measured by NIR technology and chemometrics methods. Two hundreds and thirty-nine samples were collected from two different cultivate regions and in each region three plots were chosen. NIR spectral data were acquired in the spectral region between 800 nm and 2500 nm using Nicolet FT-NIR spectrometer. Firmness was detected by a biomaterial universal testing machine. Chemometrics methods of PLS, PCR and SMLR were applied to establish statistical models for establishing the relationship between Chufa NIR spectra and MT-firmness in three different spectral regions of 800-2500 nm, 830-1250 nm and 860-1090 nm. The PLS model educed better results than PCR and SMLR models. And for the three spectral regions, the full spectral region of 800-2500 nm was better than other two. The correlation coefficient (r), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and root mean square error of cross validation (RMSECV) of the PLS model in the range of 800-2500 nm were 0.74, 4.96 N, 5.63 N and 5.38 N respectively.
Near infrared (NIR) spectroscopy is an instrumental method widely used for rapid and nondestructive detection of internal qualities of agricultural products. Statistical modeling is a very important and difficult process in NIR detection to establish the relationship between spectral information and interested index. Classical multivariate calibration methods such as partial least square regression (PLSR), principle component regression (PCR) and stepwise multi linear regression (SMLR) were often used for modeling. In this study, besides these algorithms, another mixed algorithm was adopted for establishing a nonlinear model of NIR spectra and MT-firmness of pears. The mixed algorithm was combined with SMLR and artificial neural network (ANN). Compared the classical multivariate calibration methods of PLSR, PCR and SMLR, the modeling results using PLSR method of original spectra were much better than the results using derivative spectra and the other two methods: r=0.88, RMSEC=3.79 N of calibration and r=0.83, RMSEP=4.35 N of validation. The mixed algorithm also performed better than SMLR and PCR, but was a bit worse than PLSR: r=0.85, RMSEC=4.15 N of calibration and r=0.82, RMSEP=4.67 N of validation. The results indicated that fruit NIR spectra could be used for MT-firmness prediction when proper algorithm was chosen, however, further study on statistic modeling are still needed to improve the predicting performance.
Nondestructive method of measuring soluble solids content (SSC) of kiwifruit was developed by Fourier transform near infrared (FT-NIR) reflectance and fiber optics. Also, the models describing the relationship between SSC and the NIR spectra of the fruit were developed and evaluated. To develop the models several different NIR reflectance spectra were acquired for each fruit from a commercial supermarket. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this work. The relationship between laboratory SSC and FT-NIR spectra of kiwifruits were analyzed via principle component regression (PCR) and partial least squares (PLS) regression method using TQ 6.2.1 quantitative software (Thermo Nicolet Co., USA). Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all measured spectra to reduce the effects of sample size, light scattering, noise of instrument, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and to obtain optimal calibration models. Total 480 NIR spectra were acquired from 120 kiwifruits and 90 samples were used to develop the calibration model, the rest samples were used to validate the model. Developed PLS model, which describes the relationship between SSC and NIR spectra, could predict SSC of 84 unknown samples with correlation coefficient of 0.9828 and SEP of 0.679 Brix.
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