Qualitative Analysis of Soluble Solid Content and Firmness of Pear Based on Successive Projections Algorithm and Least Square Support Vector Machine

A new combination of Monte Carlo-uninformative variable elimination and the successive projections algorithm (MC-UVE-SPA) was proposed to select the most effective variables.

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Quantitative Evaluation of Color, Firmness, and Soluble Solid Content of Korla Fragrant Pears via IRIV and LS-SVM

Agriculture ◽

10.3390/agriculture11080731 ◽

2021 ◽

Vol 11 (8) ◽

pp. 731

Author(s):

Yuanyuan Liu ◽

Tongzhao Wang ◽

Rong Su ◽

Can Hu ◽

Fei Chen ◽

...

Keyword(s):

Quality Parameters ◽

Solid Content ◽

Partial Least Square ◽

Least Square ◽

Function Parameter ◽

Support Vector ◽

Soluble Solid Content ◽

A Value ◽

Soluble Solid ◽

Error Weight

Customers pay significant attention to the organoleptic and physicochemical attributes of their food with the improvement of their living standards. In this work, near infrared hyperspectral technology was used to evaluate the one-color parameter, a*, firmness, and soluble solid content (SSC) of Korla fragrant pears. Moreover, iteratively retaining informative variables (IRIV) and least square support vector machine (LS-SVM) were applied together to construct evaluating models for their quality parameters. A set of 200 samples was chosen and its hyperspectral data were acquired by using a hyperspectral imaging system. Optimal spectral preprocessing methods were selected to obtain out partial least square regression models (PLSRs). The results show that the combination of multiplicative scatter correction (MSC) and Savitsky-Golay (S-G) is the most effective spectral preprocessing method to evaluate the quality parameters of the fruit. Different characteristic wavelengths were selected to evaluate the a* value, the firmness, and the SSC of the Korla fragrant pears, respectively, after the 6 iterations. These values were obtained via IRIV and the reverse elimination method. The correlation coefficients of the validation set of the a* value, the firmness, and the SSC measure 0.927, 0.948, and 0.953, respectively. Furthermore, the values of the regression error weight, γ, and the kernel function parameter, σ2, for the same parameters measure (8.67 × 104, 1.21 × 103), (1.45 × 104, 2.93 × 104), and (2.37 × 105, 3.80 × 103), respectively. This study demonstrates that the combination of LS-SVM and IRIV can be used to evaluate the a* value, the firmness, and the SSC of Korla fragrant pears to define their grade.

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Hyperspectral Imaging for Predicting Soluble Solid Content of Starfruit

Jurnal Teknologi ◽

10.11113/jt.v73.3480 ◽

2015 ◽

Vol 73 (1) ◽

Cited By ~ 1

Author(s):

Feri Candra ◽

Syed Abd. Rahman Abu Bakar

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Imaging System ◽

Solid Content ◽

Partial Least Square ◽

Least Square ◽

Coefficient Of Determination ◽

Soluble Solid Content ◽

Calibration Model ◽

Soluble Solid

Hyperspectral imaging technology is a powerful tool for non-destructive quality assessment of fruits. The objective of this research was to develop novel calibration model based on hyperspectral imaging to estimate soluble solid content (SSC) of starfruits. A hyperspectral imaging system, which consists of a near infrared camera, a spectrograph V10, a halogen lighting and a conveyor belt system, was used in this study to acquire hyperspectral images of the samples in visible and near infrared (500-1000 nm) regions. Partial least square (PLS) was used to build the model and to find the optimal wavelength. Two different masks were applied for obtaining the spectral data. The optimal wavelengths were evaluated using multi linear regression (MLR). The coefficient of determination (R2) for validation using the model with first mask (M1) and second mask (M2) were 0.82 and 0.80, respectively.

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Nondestructive Determination of Citric Acid Using Successive Projections Algorithm and Spectroscopic Techniques

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.9 ◽

2011 ◽

Vol 460-461 ◽

pp. 9-14

Author(s):

Fei Liu ◽

Yong He

Keyword(s):

Citric Acid ◽

Near Infrared ◽

Nir Spectroscopy ◽

Least Square ◽

Support Vector ◽

Spectroscopic Techniques ◽

Optimal Prediction ◽

Successive Projections Algorithm ◽

Svm Model ◽

Successive Projections

Successive projections algorithm (SPA) combined with least square-support vector machine (LS-SVM) was investigated to determine the citric acid of lemon vinegar by 13 wavelengths within visible/near infrared (Vis/NIR) spectral region. Five concentration levels (100%, 80%, 60%, 40% and 20%) of lemon vinegar were prepared, and the calibration set consisted of 150 samples, validation set consisted of 75 samples and the remaining 75 samples for prediction set. After the comparison of different preprocessing such as smoothing, standard normal variate and derivative, SPA was applied to extract the effective wavelengths to reduce the redundancies and collinearity of variables, and the multiple linear regression (MLR) models were developed compared with partial least squares (PLS) models. Simultaneously, the selected wavelengths were used as the inputs of LS-SVM, and a new proposed combination of SPA-LS-SVM model was developed. The results indicated that SPA-LS-SVM achieved the optimal prediction performance, and the correlation coefficient (r) and root mean square error of prediction (RMSEP) were 0.9894 and 0.0623, respectively. An excellent prediction precision was obtained. The overall results demonstrated that it was feasible to utilize Vis/NIR spectroscopy to predict the citric acid of lemon vinegar, and SPA-LS-SVM model achieved the optimal prediction precision. This study supplied a feasible method for the process monitoring of fruit vinegar manufacture and fermentation.

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Optical Absorption and Scattering Properties at 900–1650 nm and Their Relationships with Soluble Solid Content and Soluble Sugars in Apple Flesh during Storage

Foods ◽

10.3390/foods9121881 ◽

2020 ◽

Vol 9 (12) ◽

pp. 1881

Author(s):

Li Fang ◽

Kangli Wei ◽

Li Feng ◽

Kang Tu ◽

Jing Peng ◽

...

Keyword(s):

Optical Properties ◽

Soluble Sugars ◽

Solid Content ◽

Partial Least Square ◽

Least Square ◽

Coefficient Of Determination ◽

Internal Quality ◽

Integrating Sphere ◽

Soluble Solid Content ◽

Soluble Solid

Soluble solid content (SSC) is regarded as the most significant internal quality associated with the taste and maturity in fruits. Evaluating the relationship between the optical properties and soluble sugars facilitates exploration of the mechanism of optical techniques in SSC assessment. In this research, absorption coefficient (μa) and reduced scattering coefficient (μ′s) of Fuji apple during storage were obtained using automatic integrating sphere (AIS) at 905–1650 nm. Relationships between μa, μ′s and SSC, and soluble sugars contents, were investigated. The result showed that SSC, the content of total soluble sugars (TSS), fructose, glucose and sucrose were all decreasing after storage, and the same trend appeared in the change of μa and μ′s. In the whole wavelength range, both μa and μ′s were positively related to SSC and soluble sugars contents. The correlations between μa and SSC, and soluble sugars contents, showed increasing tendencies with increasing wavelengths, while for μ′s, correlations had the opposite trend. The strongest correlations between μa and SSC, and soluble sugars contents, were observed in the correlation of μa with sucrose, with an r of 0.934. Furthermore, a partial least square (PLS) model for sucrose based on μa was built with the coefficient of determination of prediction (Rp2) and the root mean square error of prediction (RMSEP) of 0.851 and 1.047, respectively. The overall results demonstrate that optical properties at the range of 905–1650 nm could be used to evaluate SSC of apples and this may due to the strong correlation between sucrose content and μa.

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Prediction of soluble solid content of starfruit using spectral imaging combined with partial least squares and support vector regression

2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) ◽

10.1109/icsipa.2015.7412225 ◽

2015 ◽

Author(s):

Feri Candra ◽

Syed Abd. Rahman Abu-Bakar

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Partial Least Squares ◽

Spectral Imaging ◽

Solid Content ◽

Support Vector ◽

Soluble Solid Content ◽

Soluble Solid

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Prediksi Indeks Panen Jambu “Kristal” secara Non Destruktif Menggunakan Portable Near Infrared Spectrometer

Jurnal Keteknikan Pertanian ◽

10.19028/jtep.09.3.103-110 ◽

2021 ◽

Vol 9 (3) ◽

pp. 103-110

Author(s):

Ayu Putri Ana ◽

Y. Aris Purwanto ◽

Slamet Widodo

Keyword(s):

Standard Error ◽

Near Infrared ◽

Quality Parameters ◽

Solid Content ◽

Partial Least Square ◽

Least Square ◽

Soluble Solid Content ◽

Soluble Solid ◽

Infrared Spectrometer ◽

Near Infrared Spectrometer

“Crystal” guava (Psidium guajava L.) is a climacteric fruit that is generally harvested by farmers based on cultivation experience. In this study, portable 740-1070 nm of near-infrared spectrometer was employed to rapidly predict harvest indices of “crystal” guava, by means of non-contact and non-destructive approach. Samples of guava fruit were collected at days after anthesis (DAS) of 91, 94, 97, and 100. The total number of each sample were 30 fruits. The firmness, soluble solid content, acidity and sugar acid ration were evaluated as quality parameters. Partial least square (PLS) method was utilized for data processing. It was found that Standard Normal Variate (SNV) resulted the best pre-processing for all quality parameters. Performances of best models were demonstrated by coefficient of corraltion (R), standard error of calibration (SEC) and standard error of prediction (SEP), which were respectively 0.88, 6.21, 5.92 for firmness prediction, 0.74, 0.84, 0.79 for soluble solid content prediction, 0.59, 0.19, 0.26 for acidity prediction, and 0.71, 1.21, 1.58 for sugar acid ratio prediction model.

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Pendugaan Kandungan Kimia Mangga Gedong Gincu Menggunakan Spektroskopi Inframerah Dekat (Prediction of Chemical Contents in ‘Gedong Gincu’ Mango using Near Infrared Spectroscopy)

Jurnal Agritech ◽

10.22146/agritech.16599 ◽

2016 ◽

Vol 36 (03) ◽

pp. 294 ◽

Cited By ~ 1

Author(s):

Herna Permata Sari ◽

Yohanes Aris Purwanto ◽

I Wayan Budiastra

Keyword(s):

Near Infrared ◽

Scatter Correction ◽

Solid Content ◽

Partial Least Square ◽

Least Square ◽

Error Prediction ◽

Soluble Solid Content ◽

Total Acid ◽

Acid Ratio ◽

Soluble Solid

The objective of this work was to predict the soluble solid content, total acid, sugar acid ratio, and crude fiber of ‘Gedong Gincu’ mango non destructive using Near infrared Spectroscopy. Experiments were carried out using 182 samples of ‘Gedong Gincu’ mango. NIR reflectance spectra measurements were performed at wavelength of 1000-2500 nm using NIRFlex N-500 fiber optic solid. References data were collected from laboratory measurements. Five pre-processing treatments, smoothing 3 points (sa3), normalization (n01), first derivative Savitzky-Golay 9 points (dg1), combination (n01, dg1), and the Multiplicative Scatter Correction (MSC) were used to improve the accuracy of the calibration model. Partial Least Square (PLS) method was used to calibrate NIR data through references data. The results show that the best method for prediction of soluble non solid spectra were MSC and 12 factor of PLS with calibration value of Correlation Coefficient (r), Square Error Calibration (SEC), Square Error Prediction (SEP), Ratio of standard error prediction to deviation (RPD) were 0.91, 0.25 %, 0.39 %, 2.14 respectively. Sugar acid ratio content was predictd using MSC and 12 factor of PLS calibration with values of r, SEC, SEP, RPD were 0.81, 32.08 °Brix/%, 38.44 °Brix/%, 1.45. Soluble solid content was predicted using sa3 and 15 factor of PLS calibration with values of r, SEC, SEP, RPD were 0.82, 1.04 °Brix, 1.28 °Brix, 1.52 respectively. Total acid was predicted using dg1 and 3 with the value of r, SEC, SEP, RPD were 0.74, 0.01 %, 0.12 %, 1.33 respectively. It could be concluded that the developed model could be used to predict the chemical contents of ‘Gedong Gincu’ mango non destructively. ABSTRAKTujuan dari penelitian ini adalah memprediksi kandungan total padatan terlarut (TPT), total asam, rasio gula asam, dan padatan tidak terlarut (serat kasar) mangga Gedong Gincu secara non destruktif menggunakan spektroskopi inframerah dekat (NIR). Bahan yang digunakan berupa mangga Gedong Gincu sebanyak 182 buah. Pengukuran spektra reflektan NIR dilakukan pada panjang gelombang 1000 – 2500 nm menggunakan NIRFlex N-500 fiber optik solid dilanjutkan pengukuran data referensi laboratorium. Lima pra-proses data spektra yaitu smoothing 3 points (sa3), normalisasi (n01), first derivative Savitzzky-golay (dg1), kombinasi (n01,dg1), dan Multiplicative Scatter Correction (MSC) dilakukan untuk meningkatkan akurasi model kalibrasi. Kalibrasi data NIR dan data kimia dilakukan menggunakan metode Partial Least Square (PLS). Metode terbaik untuk prediksi padatan tidak terlarut diperoleh dengan pra-proses MSC dan kalibrasi PLS dengan nilai Correlation Coefficient (r), Square Error Calibration (SEC), Square Error Prediction (SEP), Ratio of standard error prediction to deviation (RPD) adalah 0,91, 0,25 %, 0,39 %, 2,14, dan faktor PLS 12. Kandungan rasio gula asam diduga dengan pra-proses MSC serta kalibrasi PLS dengan nilai r, SEC, SEP, RPD adalah 0,81, 32,08 °Brix/%, 38,44 °Brix/%, 1,45 dan faktor PLS yang digunakan 12. TPT diduga menggunakan pra-proses sa3 dan kalibrasi PLS dengan nilai r, SEC, SEP, RPD adalah 0,82, 1,04 oBrix, 1,28 °Brix, 1,52. Model kalibrasi total asam diperoleh pra-proses dg1 dan kalibrasi PLS dengan nilai r, SEC, SEP, RPD adalah 0,74, 0,01 %, 0,12 %, 1,33. Hasil penelitian ini menunjukkan bahwa model yang dikembangkan dapat digunakan untuk menduga kandungan kimia mangga Gedong Gincu secara non destruktif.Kata kunci: Mangga Gedong Gincu; non destruktif; partial least square; pra-proses; spektroskopi NIR

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Two Different Portables Visible-Near Infrared and Shortwave Infrared Region for On-Tree Measurement of Soluble Solid Content of Marian Plum Fruit

Engineering Journal ◽

10.4186/ej.2020.24.5.227 ◽

2020 ◽

Vol 24 (5) ◽

pp. 227-236

Author(s):

Jetsada Posom ◽

Navavit Soonnamtiang ◽

Patcharapong Kotethum ◽

Pakhpoom Konjun ◽

Panmanas Sirisomboon ◽

...

Keyword(s):

Near Infrared ◽

Solid Content ◽

Coefficient Of Determination ◽

Support Vector ◽

Soluble Solid Content ◽

Relative Standard ◽

Soluble Solid ◽

Shortwave Infrared ◽

Mean Square Errors

The goal of this study was to predict the soluble solid content (SSC) of on-tree Marian plum fruit using two different wavelength range and algorithm. One of these was the commercial dispersion NIR spectrometer (MicroNIR 1700), providing shortwave infrared (SWIR), while the other was a making diode array spectrometer giving visible-near infrared (Vis-NIR). To search optimal model, the analytical ability of the two wavelength ranges spectrometers coupled with two algorithms: i.e. partial least squares regression (PLSR) and support vector machine regression (SVR), were investigated. Different spectral pre-processing methods were tested. The model providing the lowest root mean square errors of prediction (RMSEP) was selected. Overall, the proposed outcome was that the performance of SWIR was more accurate than Vis-NIR spectrometer, and that both SWIR and Vis-NIR coupled with PLSR algorithm had a higher accuracy than SVR algorithm. The best model for on-tree evaluation SSC was the SWIR constructed using the PLSR algorithm with the spectral pre-processing of the 2nd derivative, providing a coefficient of determination of calibration set (R2) of 0.81, a coefficient of determination of validation set (r2) of 0.76, RMSEP of 0.69 °Brix, and a relative standard error of prediction (RSEP) of 4.43%. The outcome showed that a portable SWIR spectrometer developed with PLSR could be used for monitoring the SSC of individual Marian plum fruit on-tree for quality assurance.

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