Hyperspectral Imaging for Predicting Soluble Solid Content of Starfruit

2015 ◽  
Vol 73 (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.

scholarly journals 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 ◽  
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.

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

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.

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

2016 ◽  
Vol 36 (03) ◽  
pp. 294 ◽  
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

scholarly journals Application of hyperspectral imaging for spatial prediction of soluble solid content in sweet potato

RSC Advances ◽  
2020 ◽  
Vol 10 (55) ◽  
pp. 33148-33154
Author(s):  
Yuanyuan Shao ◽  
Yi Liu ◽  
Guantao Xuan ◽  
Yongxian Wang ◽  
Zongmei Gao ◽  
...  
Keyword(s):  
Sweet Potato ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Prediction ◽  
Solid Content ◽  
Soluble Solid Content ◽  
Sweet Potatoes ◽  
Fast Detection ◽  
Soluble Solid

Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in ‘Beijing 553’ and ‘Red Banana’ sweet potatoes.

scholarly journals Evaluation of the moisture content of tapioca starch using near-infrared spectroscopy

2015 ◽  
Vol 08 (02) ◽  
pp. 1550014 ◽  
Author(s):  
Kittisak Phetpan ◽  
Panmanas Sirisomboon
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Calibration Model ◽  
Drying Process ◽  
Data Set ◽  
Prediction Ability ◽  
Tapioca Starch

The purpose of this study was to develop a calibration model to evaluate the moisture content of tapioca starch using the near-infrared (NIR) spectral data in conjunction with partial least square (PLS) regression. The prediction ability was assessed using a separate prediction data set. Three groups of tapioca starch samples were used in this study: tapioca starch cake, dried tapioca starch and combined tapioca starch. The optimum model obtained from the baseline-offset spectra of dried tapioca starch samples at the outlet of the factory drying process provided a coefficient of determination (R2), standard error of prediction (SEP), bias and residual prediction deviation (RPD) of 0.974, 0.16%, -0.092% and 7.4, respectively. The NIR spectroscopy protocol developed in this study could be a rapid method for evaluation of the moisture content of the tapioca starch in factory laboratories. It indicated the possibility of real-time online monitoring and control of the tapioca starch cake feeder in the drying process. In addition, it was determined that there was a stronger influence of the NIR absorption of both water and starch on the prediction of moisture content of the model.

Application of Long-Wave Near Infrared Hyperspectral Imaging for Measurement of Soluble Solid Content (SSC) in Pear

2016 ◽  
Vol 9 (11) ◽  
pp. 3087-3098 ◽  
Author(s):  
Jiangbo Li ◽  
Xi Tian ◽  
Wenqian Huang ◽  
Baohua Zhang ◽  
Shuxiang Fan
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Solid Content ◽  
Soluble Solid Content ◽  
Long Wave ◽  
Soluble Solid

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

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.

Quantitative Evaluation of Glycyrrhizic Acid That Affects the Product Quality of Kakkonto Extract, a Traditional Herbal Medicine, by a Chemometric near Infrared Spectroscopic Method

2009 ◽  
Vol 17 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Yoshifumi Mohri ◽  
Yukoh Sakata ◽  
Makoto Otsuka
Keyword(s):  
Acid Content ◽  
Glycyrrhizic Acid ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Wave Number ◽  
Coefficient Of Determination ◽  
Calibration Model

The purpose of this study was to construct a calibration model for the prediction of glycyrrhizic acid content in Kakkonto extracts using near infrared (NIR) spectroscopy. The NIR spectra of the Kakkonto extracts were obtained using a Fourier transform NIR spectrometer in transmission mode and chemometric analysis was performed using partial least-square (PLS) regression. The calibration model was constructed by the selection of wave number regions and by the first derivative pre-treatment of NIR spectra. The glycyrrhizic acid content could be predicted using a calibration model comprising three principal components (PCs) obtained by the PLS method. The calibration model was theoretically analysed by investigating the standard error of prediction values, the loading vectors of each PC and the regression vector. The relationship between the actual and predicted glycyrrhizic acid contents in the Kakkonto extract exhibited a straight line with a coefficient of determination of 0.966 (calibration) and 0.945 (validation), respectively. The predicted glycyrrhizic acid content in the Kakkonto extract was within the 95% predictive intervals.

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

Agriculture ◽  
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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