scholarly journals Pengembangan Sensor FT-NIR Melalui Transformasi Wavelet Untuk Evaluasi Kadar Gula Mangga Gadung (Mangifera Indica)

2017 ◽  
Vol 2 (3) ◽  
pp. 276-293
Author(s):  
Muhammad Ikhram ◽  
Zulfahrizal Zulfahrizal ◽  
Agus Arip Munawar
Keyword(s):  
Prediction Model ◽  
Wavelet Transformation ◽  
Mangifera Indica ◽  
Sugar Content ◽  
Model Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Sensor Technology ◽  
Baseline Correction ◽  
Mango Fruit

Abstrak: Sebelum adanya uji non-destruktif pada mangga untuk mengetahui kandungan kadar gula pada buah mangga selalu dilakukan metode destruktif yaitu dengan cara mangga diperas sari buahnya dan dilihat oBrix dengan menggunakan alat refraktometer. Penelitian ini bertujuan untuk mengembangkan instrument berbasis teknologi sensor FT-NIR melalui transformasi wavelet (wavelet segmentation) sehingga diharapkan dapat membantu mendeteksi cepat kualitas buah mangga. Penelitian ini menggunakan alat FT-NIR dengan sensor photodiode. Penelitian ini menggunakan model prediksi yang dibangun dengan menggunakan metode Partial least square dengan metode koreksi baseline correction. Setelah itu untuk mendeteksi data pencilan menggunakan metode analisa PCA dan hotelling T2 ellips sehingga data prediksi tidak ada noise (gangguan). Kemudian dilanjutkan dengan analisis laboratorium untuk mendapatkan nilai acuan dalam membangun model prediksi. Dalam membangun model prediksi Parameter statistika yang biasa digunakan untuk mengevaluasi model yang dihasilkan adalah Nilai Error (RMSEC), Nilai Koefisien Korelasi (r), Nilai Koefisien Determinasi (R2), dan RPD. Hasil penelitian menunjukan bahwa self developed FT-NIR mampu mendeteksi zat organik kadar gula dengan kisaran gelombang 2137 nm – 2333 nm, spektrum yang telah dikoreksi menggunakan baseline correction diperoleh nilai parameter statistiknya adalah R2 = 0,881, nilai r = 0,939, nilai RPD = 2,149, nilai error (RMSEC) sebesar 0,782. model yang dihasilkan adalah model prediksi yang bagus (good model performance) karena nilai RPD berada pada kisaran 2 - 2,5.Development of Fourier Transform Near InfraRed Spectroscopy (FT-NIR) Through Wavelet Transformation For Sugar Content Evaluation Mango Gadung (Mangifera Indica)Abstrack : Before the existence of non-destructive test on mango determine of sugar content in mango fruit always destructively by way of mango squeezed juice and seen Brix by using tool of refractometer. This research aims to develop intrument based on FT-NIR sensor technology through wavelet transformation (wavelet segmentation) so it is expected to help detect the quality of mango fruit fast. This research uses FT-NIR tool with photodiode sensor. This research uses prediction model which established by using partial least square method with correction method of baseline correction. Then proceed with laboratory analysis to obtain the reference value in building predictive model. In constructing the prediction model the usual statistical parameters used to evaluate the resulting model are error value (RMSEC), correlation coefficients (r), coefficient of determination (R2), and RPD. The results showed that self developed FT-NIR was able to detect organic subtance of sugar content with wave range 2137 nm - 2333 nm, the corrected spectra using baseline correction obtained statistic parameter value is R2 = 0,881, r = 0,939, value RPD = 2,149, error value (RMSEC) to 0,782. The model produced is a good model of performance (good model performance) because the value of RPD is in the range between 2 and 2,5.

scholarly journals Desain dan Uji Performansi Instrument Berbasis Teknologi Laser Photo-Accoustics untuk Uji Cepat Kualitas Mangga

2017 ◽  
Vol 2 (3) ◽  
pp. 308-320
Author(s):  
Aulia Ifnu Akbar ◽  
Zulfahrizal Zulfahrizal ◽  
Agus Arip Munawar
Keyword(s):  
Prediction Model ◽  
Piezoelectric Transducer ◽  
Performance Test ◽  
Sugar Content ◽  
Statistical Parameter ◽  
Partial Least Square ◽  
Least Square ◽  
Baseline Correction ◽  
Single Beam ◽  
Sugar Levels

Abstrak: Kandungan kadar gula pada buah mangga selalu dilakukan metode destruktif yaitu dengan cara mangga diperas sari buahnya dan dilihat oBrix dengan menggunakan alat refraktometer. Penelitian ini bertujuan untuk merancang alat laser Photo-Acoustics (LPAS) untuk mendeteksi cepat kadar gula pada buah mangga. Penelitian ini menggunakan alat laser He-Ne dan self developed LPAS single beam dengan sensor piezoelectric transducer dan bahan penelitian ini adalah mangga jenis udang yang diperoleh dari kebun sare Aceh Besar. Penelitian ini menggunakan model prediksi yang dibangun dengan menggunakan metode Partial least square dengan metode koreksi baseline correction. Sebelum dibangun model prediksi data pencilan dideteksi dengan metode PCA yang digandeng dengan metode Hotelling T2 ellipse, kemudian dilanjutkan dengan analisis laboratorium untuk mendapatkan nilai acuan Y dalam membangun model prediksi. Dalam membangun model prediksi Parameter statistika yang biasa digunakan untuk mengevaluasi model yang dihasilkan adalah Nilai Error (RMSEC) , Nilai Koefisien Korelasi (r), Nilai Koefisien Determinasi (R2), dan RPD. Hasil penelitian menunjukan bahwa self developed laser Photo-Accoustics yang telah di desain oleh peneliti berjalan dengan optimal terlihat dari bentuk gelombang yang dihasilkan oleh instrument pengukur osiloscope pada analisis trancient tanpa adanya noise dan terdistribusi merata. Self developed LPAS ini juga mampu mendeteksi zat organik kadar gula dengan kisaran gelombang wavenumber 5849 cm-1 – 6210 cm-1 dan 7195 cm-1 – 7559 cm-1 , spektrum yang telah dikoreksi menggunakan baseline correction diperoleh nilai parameter statistiknya adalah nilai R2 sebesar 0,7531, nilai r sebesar 0,8678 , nilai error (RMSEC) sebesar 0,4018 dan nilai RPD sebesar 1,9159. model yang dihasilkan masih dalam prediksi kasar (sufficient performance).Design and Performance Test of Laser Photo-Acoustics Based Instrument for Rapid Test of Mango Quality  Abstrack: sugar content in mango fruit always done destructively by squeezed juice and seen oBrix by using  refractometer. This study aims to design a laser Photo-Acoustics (LPAS) tool to detect rapid sugar levels in mangoes. This research uses He-Ne laser and self developed LPAS single beam with piezoelectric transducer sensor and the material of this research is shrimp mango type obtained from Aceh Besar sare . This research uses prediction model which is by using Partial least square method with baseline correction. Prior to prediction model of detected data was projected by PCA method coupled with Hotelling T2 ellipse method, then continued with laboratory analysis to obtain reference Y in constructing prediction model. In constructing the prediction model the statistical parameters commonly used to evaluate the resulting model are Error Value (RMSEC), Correlation Coefficient (r), Coefficient of Determination (R2), and RPD. The results showed that the self developed laser Photo-Accoustics that has been designed by the researcher runs optimally visible from the waveform generated by the oscilloscope measuring instrument in trancient analysis in the absence of noise and distributed evenly. Self developed LPAS is also able to detect organic substances sugar levels with wavenumber wave range 5849 cm-1 - 6210 cm-1 and 7195 cm-1 - 7559 cm-1, the spectrum has been corrected using baseline correction obtained statistical parameter value is the value of R2 equal to 0.7531, r value of 0.8678, error value (RMSEC) of 0.4018 and RPD value of 1.9159. The resulting model is still in a sufficient prediction (sufficient performance).

Is the introduction of benthic species necessary for open-water chemical reconstruction in diatom-based transfer functions?

2002 ◽  
Vol 59 (6) ◽  
pp. 938-951 ◽  
Author(s):  
Aline Philibert ◽  
Yves T Prairie
Keyword(s):  
Transfer Functions ◽  
Model Performance ◽  
Open Water ◽  
Partial Least Square ◽  
Least Square ◽  
Weighted Averaging ◽  
Benthic Species ◽  
Inference Models ◽  
Number Of Species ◽  
Chemical Conditions

Despite the overwhelming tendency in paleolimnology to use both planktonic and benthic diatoms when inferring open-water chemical conditions, it remains questionable whether all taxa are appropriate and necessary to construct useful inference models. We examined this question using a 75-lake training set from Quebec (Canada) to assess whether model performance is affected by the deletion of benthic species. Because benthic species are known to experience very different chemical conditions than their planktonic counterparts, we hypothesized that they would introduce undesirable noise in the calibration. Surprisingly, such important variables as pH, total phosphorus (TP), total nitrogen (TN), and dissolved organic carbon (DOC) were well predicted from weighted-averaging partial least square (WA-PLS) models based solely on benthic species. Similar results were obtained regardless of the depth of the lakes. Although the effective number of occurrence (N2) and the tolerance of species influenced the stability of the model residual error (jackknife), the number of species was the major factor responsible for the weaker inference models when based on planktonic diatoms alone. Indeed, when controlled for the number of species in WA-PLS models, individual planktonic diatom species showed superior predictive power over individual benthic species in inferring open-water chemical conditions.

scholarly journals Error propagation in spectrometric functions of soil organic carbon

SOIL ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 275-288
Author(s):  
Monja Ellinger ◽  
Ines Merbach ◽  
Ulrike Werban ◽  
Mareike Ließ
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Error Propagation ◽  
Field Experiments ◽  
Model Building ◽  
Model Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Measurement Protocol

Abstract. Soil organic carbon (SOC) plays a major role concerning chemical, physical, and biological soil properties and functions. To get a better understanding of how soil management affects the SOC content, the precise monitoring of SOC on long-term field experiments (LTFEs) is needed. Visible and near-infrared (Vis–NIR) reflectance spectrometry provides an inexpensive and fast opportunity to complement conventional SOC analysis and has often been used to predict SOC. For this study, 100 soil samples were collected at an LTFE in central Germany by two different sampling designs. SOC values ranged between 1.5 % and 2.9 %. Regression models were built using partial least square regression (PLSR). In order to build robust models, a nested repeated 5-fold group cross-validation (CV) approach was used, which comprised model tuning and evaluation. Various aspects that influence the obtained error measure were analysed and discussed. Four pre-processing methods were compared in order to extract information regarding SOC from the spectra. Finally, the best model performance which did not consider error propagation corresponded to a mean RMSEMV of 0.12 % SOC (R2=0.86). This model performance was impaired by ΔRMSEMV=0.04 % SOC while considering input data uncertainties (ΔR2=0.09), and by ΔRMSEMV=0.12 % SOC (ΔR2=0.17) considering an inappropriate pre-processing. The effect of the sampling design amounted to a ΔRMSEMV of 0.02 % SOC (ΔR2=0.05). Overall, we emphasize the necessity of transparent and precise documentation of the measurement protocol, the model building, and validation procedure in order to assess model performance in a comprehensive way and allow for a comparison between publications. The consideration of uncertainty propagation is essential when applying Vis–NIR spectrometry for soil monitoring.

scholarly journals A non-destructive method to quantify leaf starch content in red clover

2020 ◽  
Author(s):  
Lea Antonia Frey ◽  
Philipp Baumann ◽  
Helge Aasen ◽  
Bruno Studer ◽  
Roland Kölliker
Keyword(s):  
Energy Content ◽  
Starch Content ◽  
Model Performance ◽  
Red Clover ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Test Set ◽  
Independent Test ◽  
Non Destructive

Abstract Background Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes.Results We assessed prediction performance of partial least square regression models (PLSR) and validated model performance with an independent test set. Starch content of the training set ranged from 0.1 to 120.3 mg g -1 DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g -1 DW. Model performance decreased when applied to the independent test set (RMSE = 29 mg g -1 DW, R 2 = 0.36). Different filtering methods did not increase model performance.Conclusion The non-destructive spectral method presented here, provides a tool to detect large differences in leaf starch content of red clover. Breeding material can be sampled and selected according to their starch content without destroying the plant.

scholarly journals Using Infrared Spectroscopy for Tracking and Estimating Antioxidant in Tomato Fruit Fractions

2018 ◽  
Vol 3 (5) ◽  
pp. 21
Author(s):  
Ayman Ibrahim ◽  
Hussin Daood ◽  
Zsuzsanna Bori ◽  
Lajos Helyes
Keyword(s):  
Infrared Spectroscopy ◽  
Prediction Model ◽  
Standard Error ◽  
Tomato Fruit ◽  
Partial Least Square ◽  
Least Square ◽  
Internal Quality ◽  
Color Analysis ◽  
Quantum Leap ◽  
Β Carotene

Infrared technology has brought a quantum leap in the specialization of non-destructive systems for internal quality inspection of agricultural and food products. Applying near-infrared spectroscopy technique (NIRs) for tracking and estimating some antioxidants such as (Lycopene, β-carotene, Phytoene and Phytofluenxe) in tomato fruit fractions (Exocarp, Mesocarp, Endocarp and Tomato pomace) with prediction model. High-performance liquid chromatography (HPLC) device showed the antioxidant concentrations values within tomato fractions. Where, the maximum and minimum values observed in the mesocarp and exocarp fractions. Also, tomato fractions color analysis confirmed these results. Meanwhile, mesocarp fraction within range dark red color with h°≈ 31.7°, due to increased lycopene concentration, whereas, exocarp fraction was 77.29° for h°, within yellow range. In addition to HPLC and color reference methods were consensus significantly with the different of spectral transformations by the regression of partial least square (PLS). NIR spectra and antioxidant in tomato fractions were taken to establish calibration models for tracking and estimating antioxidant in tomato fractions by using partial least squares (PLS) model. The obtained Coefficients of prediction model (R2p) were 0.95, 0.91, 0.93 and 0.94 for Lycopene, β-Carotene, Phytoene and Phytofluenxe respectively. The values of (RPD) ratio obtained from the standard deviation to the standard error of prediction and also (RER) obtained from the standard error range of prediction model were varied for different tomato fractions and antioxidant content, and found that the NIR model suitable not only for screening the different concentrations values of antioxidants for tomato fractions, but also suitable for most applications including quality assurance. 

scholarly journals In-Situ Screening of Soybean Quality with a Novel Handheld Near-Infrared Sensor

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6283
Author(s):  
Didem Peren Aykas ◽  
Christopher Ball ◽  
Amanda Sia ◽  
Kuanrong Zhu ◽  
Mei-Ling Shotts ◽  
...  
Keyword(s):  
Amino Acids ◽  
Fatty Acid ◽  
Moisture Content ◽  
Real Time ◽  
Crude Protein ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sensor Technology

This study evaluates a novel handheld sensor technology coupled with pattern recognition to provide real-time screening of several soybean traits for breeders and farmers, namely protein and fat quality. We developed predictive regression models that can quantify soybean quality traits based on near-infrared (NIR) spectra acquired by a handheld instrument. This system has been utilized to measure crude protein, essential amino acids (lysine, threonine, methionine, tryptophan, and cysteine) composition, total fat, the profile of major fatty acids, and moisture content in soybeans (n = 107), and soy products including soy isolates, soy concentrates, and soy supplement drink powders (n = 15). Reference quantification of crude protein content used the Dumas combustion method (AOAC 992.23), and individual amino acids were determined using traditional protein hydrolysis (AOAC 982.30). Fat and moisture content were determined by Soxhlet (AOAC 945.16) and Karl Fischer methods, respectively, and fatty acid composition via gas chromatography-fatty acid methyl esterification. Predictive models were built and validated using ground soybean and soy products. Robust partial least square regression (PLSR) models predicted all measured quality parameters with high integrity of fit (RPre ≥ 0.92), low root mean square error of prediction (0.02–3.07%), and high predictive performance (RPD range 2.4–8.8, RER range 7.5–29.2). Our study demonstrated that a handheld NIR sensor can supplant expensive laboratory testing that can take weeks to produce results and provide soybean breeders and growers with a rapid, accurate, and non-destructive tool that can be used in the field for real-time analysis of soybeans to facilitate faster decision-making.

Tolerance of near Infrared Calibrations to Temperature Variations; A Practical Evaluation

2000 ◽  
Vol 8 (2) ◽  
pp. 125-132 ◽  
Author(s):  
W.G. Hansen ◽  
S.C.C. Wiedemann ◽  
M. Snieder ◽  
V.A.L. Wortel
Keyword(s):  
Near Infrared ◽  
Model Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Temperature Variations ◽  
Precise Control ◽  
Practical Evaluation ◽  
Temperature Variable ◽  
Value Determination ◽  
Model Robustness

Process samples of esters have been analysed by transmittance near infrared (NIR) at temperatures between 60 and 70°C (±0.2). Apart from density changes, these small temperature variations affect molecular associations by H-bonding. Partial Least Square (PLS) models based on the first OH overtone (1350–1500nm) have been made for hydroxyl value determination, including implicitly the temperature variable. The sensitivity of these NIR calibrations to temperature has been evaluated by an analysis of variance and the “Taguchi principle”, using both average model performance and model variance. An accurate and precise control of the sample temperature prior to scanning leads to the lowest prediction error. When temperature fluctuations can not be avoided, introduction of temperature variance in the calibration set can improve the model robustness; this strategy is only beneficial if temperature range, temperature distribution and number of PLS factors have been carefully optimised.

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