Rapid Starch Evaluation in Fresh Cassava Root Using a Developed Portable Visible and Near-Infrared Spectrometer

Bantadjan, Yuranan; Rittiron, Ronnarit; Malithong, Kritsanun; Narongwongwattana, Sureeporn

doi:10.1021/acsomega.0c01346

Cited by 17 publications

(18 citation statements)

References 19 publications

(29 reference statements)

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“…The precision of the calibration equation should be checked for accuracy to confirm the implementation of the NIR method. , The calibration equations developed in the current work showed that this method could be useful to predict the concentrations of profenofos residues in three vegetables (Chinese kale, cabbage, and green chili spur pepper) at levels greater than 30 mg/kg since there were no significant differences between the FT-NIR-predicted values and actual values (from GC–MS) at a confidence interval of 95%. All statistics indicated that the calibration equations were acceptable for good performance in FT-NIR measurement.…”

Section: Resultsmentioning

confidence: 81%

Detection of Profenofos in Chinese Kale, Cabbage, and Chili Spur Pepper Using Fourier Transform Near-Infrared and Fourier Transform Mid-Infrared Spectroscopies

et al. 2021

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Different types of quantitative technology based on infrared spectroscopy to detect profenofos were compared based on Fourier transform near-infrared (FT-NIR; 12,500–4000 cm–1) and Fourier transform mid-infrared (FT-MIR; 4000–400 cm–1) spectroscopies. Standard solutions in the range of 0.1–100 mg/L combined with the dry-extract system for infrared (DESIR) technique were analyzed. Based on partial least-squares regression (PLSR) to develop a calibration equation, FT-NIR–PLSR produced the best prediction of profenofos residues based on the values for R 2 (0.87), standard error of prediction or SEP (11.68 mg/L), root-mean-square error of prediction or RMSEP (11.50 mg/L), bias (−0.81 mg/L), and ratio performance to deviation or RPD (2.81). In addition, FT-MIR–PLSR produced the best prediction of profenofos residues based on the values for R 2 (0.83), SEP (13.10 mg/L), RMSEP (13.00 mg/L), bias (1.46 mg/L), and RPD (2.49). Based on the ease of use and appropriate sample preparation, FT-NIR–PLSR combined with DESIR was chosen to detect profenofos in Chinese kale, cabbage, and chili spur pepper at concentrations of 0.53–106.28 mg/kg. The quick, easy, cheap, effective, rugged, and safe technique coupled with gas chromatography–mass spectrometry was used to obtain the actual values. The best FT-NIR–PLSR equation provided good profenofos detection in all vegetables based on values for R 2 (0.88–0.97), SEP (5.27–11.07 mg/kg), RMSEP (5.25–11.00 mg/kg), bias (−1.39 to 1.30 mg/kg), and RPD (2.91–5.22). These statistics revealed no significant differences between the FT-NIR predicted values and actual values at a confidence interval of 95%, with agreeable results presented at pesticide residue levels over 30 mg/kg. FT-NIR spectroscopy combined with DESIR and PLSR should be considered as a promising screening method for pesticide detection in vegetables.

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Section: Resultsmentioning

confidence: 81%

Detection of Profenofos in Chinese Kale, Cabbage, and Chili Spur Pepper Using Fourier Transform Near-Infrared and Fourier Transform Mid-Infrared Spectroscopies

et al. 2021

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“…Absorption peaks characteristic of starch content were also found at 423, 450, 480, and 504 nm. Meanwhile, SSC related peaks were discovered at 636 nm, and this was the amylose waveband ( Bantadjan et al., 2020b ). The RC peaks for water content appeared at 759 nm (third overtone of the O–H band) and 975 nm (second overtone of the O–H band), corresponding to water molecules ( Phetpan et al., 2018 ; Posom et al., 2020 ; Bantadjan et al., 2020a ).…”

Section: Resultsmentioning

confidence: 99%

Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy

Kusumiyati

Hadiwijaya

Putri

et al. 2021

Heliyon

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Latest studies on Vis/NIR research mostly focused on particular products. Developing a model for a specific product is costly and laborious. This study utilized visible/near-infrared (Vis/NIR) spectroscopy to evaluate the quality attributes of six products of the Cucurbitaceae family, with a single estimation model, rather than individually. The study made use of six intact products, zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber. Subsequently, the multi-product models for soluble solids content (SSC) and water content were created using partial least squares regression (PLSR) method. The PLSR modeling produced satisfactory results, the coefficient of determination in calibration set (R 2 c) was discovered to be 0.95 and 0.92, while the root mean squares error of calibration (RMSEC) was found to be 0.41 and 0.61, for SSC and water content, respectively. These models were able to accurately predict the unknown samples with coefficient of determination in prediction set (R 2 p) of 0.96 and 0.92, as well as root mean squares error of prediction (RMSEP) of 0.32 and 0.58, while the ratio of prediction to deviation (RPD) was found to be 5.68 and 3.69 for SSC and water content, respectively. This shows Vis/NIR spectroscopy was able to quantify the SSC and water content of six products of Cucurbitaceae family, using a single model.

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“…The best model, which was obtained from the optimum prediction results, was further evaluated for accuracy. Many previous reports suggest that model performance using NIRS should be verified according to the standard ISO method. , ISO 12099:2017 gives guidelines for near-infrared spectrometry of animal feeding stuffs, cereals, and milled cereal products, which state that the performance of a prediction model shall be determined from a set of validation samples and based on the statistical parameters, bias, SEP, and slope. The significance of the bias is checked by a t -test using the following formula where α is the probability of making a type I error; t is the appropriate student t -value for a two-tailed test with degrees of freedom associated with SEP and the selected probability of a type I error; n is the number of independent samples; and SEP is the standard error of prediction.…”

Section: Methodsmentioning

confidence: 99%

“…Many previous reports suggest that model performance using NIRS should be verified according to the standard ISO method. 25,43 ISO 12099:2017 gives guidelines for near-infrared spectrometry 44 of animal feeding stuffs, cereals, and milled cereal products, which state that the performance of a prediction model shall be determined from a set of validation samples and based on the statistical parameters, bias, SEP, and slope. The significance of the bias is checked by a t-test using the following formula…”

Section: Methodsmentioning

confidence: 99%

Determination of Adenosine and Cordycepin Concentrations in Cordyceps militaris Fruiting Bodies Using Near-Infrared Spectroscopy

et al. 2020

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Near-infrared (NIRS) spectroscopy, coupled with partial least squares regression, was used to predict adenosine and cordycepin concentrations in fruiting bodies of Cordyceps militaris . The fruiting body samples were prepared in four different sample formats, which were intact fruiting bodies, chopped fruiting bodies, dried powder, and dried crude extract. The actual amount of the adenosine and cordycepin concentrations in fresh fruiting bodies was analyzed by high-performance liquid chromatography. Results showed that the prediction models developed from the chopped samples provided excellent accuracy in both parameters with minimal sample preparation. These optimum models provided a coefficient of determination of prediction, standard error of prediction, bias, and residual predictive deviation, which were respectively 0.95, 16.60 mg kg –1 , –8.57 mg kg –1 , and 5.04 for adenosine prediction, and 0.98, 181.56 mg kg –1 , –1.05 mg kg –1 , and 8.9 for cordycepin prediction. The accuracy and performance of the model were determined by ISO12099:2017(E). It was found that these two equations can be considered to be acceptable at a probability level of 95% confidence. The NIRS technique, therefore, has the potential to be an objective method for determining the adenosine and cordycepin concentrations in C. militaris fruiting bodies.

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Rapid Starch Evaluation in Fresh Cassava Root Using a Developed Portable Visible and Near-Infrared Spectrometer

Cited by 17 publications

References 19 publications

Detection of Profenofos in Chinese Kale, Cabbage, and Chili Spur Pepper Using Fourier Transform Near-Infrared and Fourier Transform Mid-Infrared Spectroscopies

Detection of Profenofos in Chinese Kale, Cabbage, and Chili Spur Pepper Using Fourier Transform Near-Infrared and Fourier Transform Mid-Infrared Spectroscopies

Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy

Determination of Adenosine and Cordycepin Concentrations in Cordyceps militaris Fruiting Bodies Using Near-Infrared Spectroscopy

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