Study on predicting soluble solid content of peaches using near infrared diffuse reflection spectroscopy
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The nondestructive method for evaluating soluble solid content(SSC)of intact peach using diffuse near infrared reflectance was investigated.The prediction model was established by modified partial least squares(modified PLS)analysis with spectral and constituent measurement of calibration sample of 150 peach fruit.The results of validation with 20 peach fruit showed that the first derivative spectra with modified PLS provided the better predication of SSC of peach fruit with the bias of 0.381 between predicted and measured values,the standard error of prediction of 0.427 and the correlation coefficient of 0.701.It suggested that the diffuse near infrared reflectance technique be feasible for nondestructive detection of soluble solid content of peach fruit in the wave number range of 600~1848nm.Keywords:
Diffuse reflection
Derivative (finance)
Content (measure theory)
Reflection
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【Objective】 The objectives of the study were to predict sugar content and pH value of pear fruit by using the near-infrared diffuse reflectance(NIR) spectra.【Method】 After three point windows moving smooth treatment,the first derivative D1log(1/R) and multivariant scattering correction were used to preprocess the primitive spectrum(350-1 800 nm) of pear fruit respectively.Multi-linear regression(MLR),principal component regression(PCR) and partial least square(PLS) regression were taken to build prediction models which were used to quantitatively analyze sugar content and pH value of pear fruit respectively.【Result】 The PLS model with D1log(1/R) data treatment is prior to the other two ways based on the comparative analysis.The results show that the correlation coefficients of sugar and pH value are 0.928 5 and 0.858 4 respectively,and root mean standard error of sugar and pH is 0.436 4 and 0.120 5 respectively.【Conclusion】 The research indicates that NIR spectroscopy could provide an accurate,reliable and nondestructive method for assessing the internal quality index sugar content and pH value of pear fruit.
Principal component regression
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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.
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An analytical procedure has been developed for the nondestructive measurement of soluble solids content in tomato samples using near infrared diffuse reflection spectrum,The original spectrum averaged was preprocessed by 10 different spectral data preprocessing methods,The result showed that the constant offset elimination data preprocessing method is the best,the frequency range of spectroscopy is 11 998.9cm-1～5 449.8cm-1 and 4 601.3 cm-1～4 246.5 cm-1,which can represent the information of SSC in tomatoes.The quantitative analysis model of soluble solids content in tomato samples was built based on Partial least squares(PLS) and its correlation coefficient between prediction and actual value is 0.954,yielding standard error of calibration(SEC)=0.321,standard error of prediction(SEP)=0.475%.This NIR method is reliable for determining soluble solids content in tomatoes nondestructively.
Content (measure theory)
Diffuse reflection
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In order to search for an appropriate preprocessing method for the nondestructive measurement of soluble solids content in tomato samples by using near infrared diffuse reflection spectroscopy,the average spectroscopy method was compared with 10 different spectra data preprocessing methods.The most significant r(0.954) was found with the constant offset elimination data preprocessing method based on PLS.The standard error of calibration(SEC) was 0.321%,the best wave band was 11 998.9~5 449.8 cm-1 and 4 601.3~4 246.5 cm-1,which can represent the information of SSC in tomatoes.The result shows that the constant offset elimination was an efficient preprocessing method on nondestructive measurement of soluble solids content in tomato samples by using near infrared diffuse reflection spectroscopy.
Diffuse reflection
Attenuated total reflection
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Near infrared transmittance spectroscopy ranging from 643.26nm to 928.35nm was investigated for its feasibility to determine the soluble solid content of apple nondestructively. The characteristic wavelength bands of SSC were searched by interval partial least square (iPLS) regression, The detection of near-infrared spectroscopy models of SSC were developed. Calibration models of the soluble solids content of apple gave the correlation coefficient of 0.937875, with a standard error of calibration of 0.380498, a standard error of prediction of 0.463898, and a bias of -0.001484. The study shows that the Near Spectroscopy was feasible to nondestructively measure the sugar content of apple.
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Ultraviolet visible spectroscopy
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The objectives of the study were to predict soluble solid content and firmness of pear fruit by using the near-infrared diffuse reflectance(NIR) spectra.The first derivative D1log(1/R) and the second derivative D1log(1/R) were used to preprocess the primitive spectrum of pear fruit respectively.Multi-linear regression(MLR),principal component regression(PCR) and partial least square(PLS) regression were used to build prediction models which were used to quantitatively analyze soluble solid content and firmness of pear fruit respectively.The PLS model with D1log(1/R) data treatment was prior to the other two ways based on the comparative analysis.The results show that the correlation coefficients of soluble solid content and firmness are 0.9306 and 0.8478 respectively,and root mean standard error of soluble solid content and firmness are 0.42064°Birx and 1.277 respectively.The research indicates that NIR spectroscopy could provide an accurate,reliable and nondestructive method for assessing the quality index soluble solid content and firmness of pear fruit.
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Principal component regression
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To establish mathematical relationship between near infrared diffuse reflection(NIR)spectroscopy and internal qualities of sweet persimmon, and to evaluate it. In this paper, soluble solids and hardness as the evaluation index, in the spectral region between 400~2500 nm, to establish calibration model for storage period at room temperature, at cold temperature and complex respectively.Using optimal model to predict 40 unknown samples, the results showed that the modified partial least squares(MPLS) model, with respect to the first derivative D1 lg(1/R) and weighted multiple scatter correction, provided better prediction performance for SSC and firmness of sweet persimmon, with the root mean square error of prediction(SEP) of 0.376, 0.055 respectively, the correlation coefficient of prediction(R P 2) of 0.832, 0.866 respectively, ratio performance deviation(RPD) of 2.416, 2.418 respectively. The preliminary research on the built models indicated that nondestructive measurement of internal qualities of sweet persimmon in different storage using NIR spectroscopy technique was feasible.
Diffuse reflection
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Attenuated total reflection
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Soluble solids content (SSC) is one of the most important quality attributes of navel oranges, either for fresh or for processing market. Since, SSC can be measured only destructively, the results are representative only if carried out on large samples and do not allow classifying marketable fruit one by one according to their specific SSC. As a nondestructive method, Vis/NIR measurement of SSC of fruits is widely studied all over the world. The objective of this research is to determine how Vis/NIR measurements of SSC of navel oranges were affected by spectra pretreatment and various spectroscopic ranges (Vis:400-799 nm; NIR1:800-999 nm; NIR2:1000-1800 mn) using partial least squares (PLS) regression. Spectra correction algorithms, such as multiplication scatter correction (MSC), first derivative (FD) and second derivative (SD) were used and evaluated in this work. The FD correction and PLS produced best noise removing capability and obtained optimal calibration models. This model could predict SSC of navel oranges of prediction set with correlation coefficient (Rpre) of 0.8553 and standard errors of prediction (RMSEP) of 1.6330.
Navel orange
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The potential of using visible and near infrared diffuse reflectance spectroscopy to assess soluble solids content (SSC) of intact navel orange was examined. A total 40 samples were used to develop the calibration and prediction models. NIR spectral data were collected in the spectral region between 350 and 2 500 nm and their second derivative spectra were used for the present study. Different scattering correction algorithms (no preprocessing and multiplicative scattering correction (MSC)) were compared. Camibration models based on different spectral ranges, different derivatives and different kinds of statistical models including partial least square (PLS) and principal component regression (PCR) were also compared. The best results of PLS models with the second derivative spectra are r = 0.929, RMSEC = 0.517 and RMSEP = 0.592 in the wavelength range of 361-2 488 nm. The results show that this method is feasible for non destructive assessing of SSC of the navel orange.
Navel orange
Diffuse reflection
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The objectives of the present study were to establish the relationships between the near-infrared diffuse reflectance (NIR) spectra and the soluble solids content (SSC) of internal quality index of pear fruit, and to evaluate the value of NIR spectrometry in measuring the SSC of internal quality index of pear fruit. NIR spectroscopy in the 350-1 800 nm range was used to analyze the SSC of internal quality index of pear fruit with multi-linear regression (MLR), principal component regression (PCR) and partial least square (PLS) regression. Meanwhile, the best combinations of different positions at pear fruit, the logarithms of the reflectance reciprocal log(1/R), its first derivative D1 log(1/R) and second derivative D2 log(1/R) were investigated. The best prediction results, based on the comparative analysis, were obtained with the PLS model and D1 log(1/R) at equatorial position of pear fruit. The results show that the predictions with PLS models, based on D1 log(1/R) at equatorial position of pear fruit, are correlation coefficients (R(p)) of 0.851 7 and root mean standard error of prediction (RMSEP) of 0.879 3 for SSC. The preliminary research on the built models indicates that NIR spectroscopy could provide an accurate, reliable and nondestructive method for assessing the SSC of internal quality index of pear fruit.
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