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.
Near infrared (NIR) spectroscopy is an ideal analytical method for rapid and nondestrctive measurement of the properties of agriculture products. The efficient use of this method is dependent on multivariate calibration methods determined by the sensitivity to variations. However, fluctuation of background and noise are unavoidable during collecting spectra, which will not only worsen the precision of prediction, but also complicate the multivariate models. Therefore, the first step of a multivariate calibration based on NIR spectra data is often to preprocess the data for the purpose of removing the varying background and noise. In this study, wavelet transform (WT) was used to eliminate the varying background and noise simultaneously in the near infrared spectroscopy signals of 55 navel oranges. Three families of mother wavelets (Symlets, Daubechies and Coiflet), four threshold selection rules (Rigrsure, Heursure, Minimaxi, Fixed form threshold), and two threshold functions (soft and hard) were applied to estimate the performances. The sugar content of intact navel orange was calculated by partial least squares regression (PLSR) with the reconstructed spectra after denoised. The results show that the best denoising performance was reached via the combination of Daubechies 5, "Fixed form" threshold selection rule, and hard threshold function. Based on the optimization parameter, wavelet regression models on sugar content in navel orange were also developed and resulted in a smaller prediction error than a traditional PLSR model.
Visible/near infrared spectroscopy on-line determination had been widely used in agricultural products and food samples non-destructive internal quality determination. This research proposed to design real-time determination software in order to estimate soluble solids content (SSC) of fruit on line. Functions of the software included real-time spectroscopy pre-processing, real-time spectroscopy viewing, model building, SSC estimating, etc. In addition, Fenghua juicy peaches were used to validate the practicability and the real-time capability. And SSCs of peach samples were predicted by the software on line. The research provided some help to the real-time non-destructive internal quality determination of the fruit. As the important part of the real-time determination, the determination method and technology were fully accordance with the need at real-time and model's precision.
In this study, two measuring systems for chlorophyll content of tomato leaves were developed based on near-infrared spectral techniques. The systems mainly consists of a FT-IR spectrum analyzer, optic fiber diffuses reflection accessories and data card. Diffuse reflectance of intact tomato leaves was measured by an optics fiber optic fiber diffuses reflection accessory and a smart diffuses reflection accessory. Calibration models were developed from spectral and constituent measurements. 90 samples served as the calibration sets and 30 samples served as the validation sets. Partial least squares (PLS) and principal component regression (PCR) technique were used to develop the prediction models by different data preprocessing. The best model for chlorophyll content had a high correlation efficient of 0.9348 and a low standard error of prediction RMSEP of 4.79 when we select full range (12500-4000 cm-1), MSC path length correction method by the log(1/R). The results of this study suggest that FT-NIR method can be feasible to detect chlorophyll content of tomato leaves rapidly and nondestructively.
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.
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.
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.
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.
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 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.
KEYWORDS: Wavelets, Calibration, Interference (communication), Near infrared, Wavelet transforms, Signal to noise ratio, Spectroscopy, Data modeling, Chemical analysis, Infrared spectroscopy
A new method is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of Fourier transform near infrared (FT-NIR) spectral signals. An ideal spectrum signal prototype was constructed based on the FT-NIR spectrum of fruit sugar content measurement. The performances of wavelet based threshold de-noising approaches via different combinations of wavelet base functions were compared. Three families of wavelet base function (Daubechies, Symlets and Coiflets) were applied to estimate the performance of those wavelet bases and threshold selection rules by a series of experiments. The experimental results show that the best de-noising performance is reached via the combinations of Daubechies 4 or Symlet 4 wavelet base function. Based on the optimization parameter, wavelet regression models for sugar content of pear were also developed and result in a smaller prediction error than a traditional Partial Least Squares Regression (PLSR) mode.
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 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.
The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.
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.
The feasibility of Fourier transform near infrared (FT-NIR) spectroscopic technology for rapid quantifying pear internal quality in different growing stage was investigated. A total of 248 pear samples collected at different harvest time (pre-harvest, mid-harvest and late harvest time) were used to develop the calibration models. The quality indices included soluble solids content (SSC) and titratable acidity (TA). Partial least squares (PLS) regression and principle component regression (PCR) regression were carried out describing relationships between the data sets of laboratory data and the FT-NIR spectra. Besides cross and test set validation, the established models were subjected to a further evaluation step by means of additional pear samples with unknown internal quality. Models based on the different spectral ranges and with several data pre-processing techniques (smoothing, multiplicative signal correction, standard normal variate, etc), were also compared in this research. Performance of different models was assessed in terms of root mean square errors of prediction (RMSEP) and correlation coefficients (r) of validation set of samples. The best predictive models had a RMSEP of 0.320, 0.019 and correlation coefficient (r) equal to 0.93, 0.89 for SSC and TA, respectively. Results indicated that FT- NIR spectroscopy could be an easy to facilitate, reliable, accurate and fast method for non-destructive evaluation of pears maturity.
This work evaluates the feasibility of Fourier transform near infrared (FT-NIR) spectrometry for rapid determining the total soluble solids content and acidity of apple fruit. Intact apple fruit were measured by reflectance FT-NIR in 800-2500 nm range. FT-NIR models were developed based on partial least square (PLS) regression and principal component regress (PCR) with respect to the reflectance and its first derivative, the logarithms of the reflectance reciprocal and its second derivative. The above regression models, related the FT-NIR spectra to soluble solids content (SSC), titratable acidity (TA) and available acidity (pH). The best combination, based on the prediction results, was PLS models with respect to the logarithms of the reflectance reciprocal. Predictions with PLS models resulted standard errors of prediction (SEP) of 0.455, 0.044 and 0.068, and correlation coefficients of 0.968, 0.728 and 0.831 for SSC, TA and pH, respectively. It was concluded that by using the FT-NIR spectrometry measurement system, in the appropriate spectral range, it is possible to nondestructively assess the maturity factors of apple fruit.
Radial basis function networks (RBFN) have been widely used for function approximation and pattern classification as an alternative to conventional artificial neural networks. In this paper, reflectance spectroscopy and chemical measurements of total soluble solids (TSS)content were used to develop a nondestructive technique for predicting the TSS and a relationship was also established between the TSS content in pears determined by diffuse reflectance spectra (4200-12500cm-1) and by chemical measurements. The effectiveness of the radial basis function networks of nonlinear calibration model was presented and compared with the linear algorithms of the partial least squares calibration models. The results show that the relatively coefficient of determination (r) of prediction obtained with linear partial least squares and the nonlinear radial basis function networks are 0.72, 0.83 and the root mean square error of prediction are 0.49, 0.45 respectively. Our results revealed that the calibration model of radial basis function networks produced better prediction of TSS than the model of partial least squares when the samples consist of multi-components.
Wavelet transform (WT) has proven a powerful and efficient tool for dealing with chemical data due to its characteristic of dual localization and has been widely used in analytical chemistry. This paper aims at serving three purposes: First, it gives a review of the applications of the wavelet transform in infrared spectroscopy; Second, it gives a quick summary of aspects and properties of wavelets and wavelet transforms which are needed in order to understand how to (pre-) process data from spectrometry with wavelet methods; Third, it shows on a typical example (apple NIR spectra) how wavelet transforms can be used in order to extract quantitative information. The sugar content of intact apple was measured by NIRS and analyzed by wavelet transform, which is a new development in signal treatment method in recent years. The results show that the spectra treated with wavelet transform indicate more effectively the relationship with sugar content in intact apple. Compared with original spectra, wavelet transform of three-size has the most marked relation with sugar content. The predicting precision of five-element regression is the best and the scale 3 is the best for its 0.904 correlation efficient of determination and the 0.777 in standard error of prediction which is less than that of primitive spectra. Therefore, the conclusion of improved predicting precision for quantitative detection of sugar content in intact apple with wavelet transform can be drawn.
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