Non-destructive Measurement of Lycopene Content in High Soluble Solids Stored Tomato (Solanum Lycopersicum Mill. cv Rinka 409)

Anggraeni, Fanesya Dyah; Khuriyati, Nafis; Falah, Mohammad Affan Fajar; Nishina, Hiroshige; Takayama, Kotaro; Takahashi, Noriko

doi:10.18517/ijaseit.10.6.9478

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…In addition, PLS is a non-parametric, multivariable approach for predicting the calibration model that assigns the response variable y to an x-predictor group [26], [27]. Based on research conducted by Anggraeni [28] and Hayati [29] on the comparison of PLS methods with principal component regression (PCR), it was shown that the PLS method provides optimum results. The reference and spectra data were used to develop calibration models for determining the total carotene and vitamin C.…”

Section: F Data Analysismentioning

confidence: 99%

Determination of Total Carotene and Vitamin C in Chili Powder (Capsicum annuum L.) Non-destructively Using Near-Infrared Spectroscopy

Khuriyati¹,

Sukartiko²,

Falah³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Chili (Capsicum annuum L.) is an important source of total carotene and vitamin C. Both substances are widely used in food processing materials, supporting a healthy immune system and medicine. However, destructive testing often obtains information about the substances, which damages the tested material and requires a relatively long analysis. Therefore, this research aims to develop calibration models of total carotene and vitamin C in chili powder for non-destructive testing using near-infrared spectroscopy. The samples consist of four groups of color, i.e., light green, dark green, red tinge, and red, with a total of 84 samples. Seventy percent of the sample was used for calibration, while the rest of the sample was used for validation. Spectra were measured using the NIRFlex N-500 instrument at a wavelength of 1000 nm to 2500 nm and analyzed with the partial least square (PLS) method using three spectral pre-treatments, which are multiplicative scatter correction (MSC), first derivative savitzky-golay, and de-trending. The accuracy and model reliability was determined by the coefficient of determination (R 2 ) and the residual predictive deviation (RPD). The best calibration models were successfully obtained when the spectrum was processed using the first derivative savitzky-golay pre-treatment with 6 and 5 PLS factors for vitamin C and total carotene, respectively. Both models were accurate and can be potentially used for determining the total carotene and vitamin C in chili powder samples non-destructively.

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Section: F Data Analysismentioning

confidence: 99%

Determination of Total Carotene and Vitamin C in Chili Powder (Capsicum annuum L.) Non-destructively Using Near-Infrared Spectroscopy

Khuriyati¹,

Sukartiko²,

Falah³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Horticultural products can also use this NIR method in grading, sorting, internal quality, and determining harvest time [5]. Determination of the quality of horticultural products can be done non-destructively by using NIR spectroscopy that has been applied to watermelon [6] and melon [7], using spectral for detection Lycopene Content in Tomato [8], detection of mango quality [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Effects of Oversampling Smote and Spectral Transformations in the Classification of Mango Cultivars Using Near-Infrared Spectroscopy

Khumaidi¹,

Raafi'udin²

2022

International Journal on Advanced Science, Engineering and Information Technology

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Near-Infrared spectroscopy (NIR) is a non-destructive analytical technique that can provide chemical and structural information on samples in a speedy and accurate time. NIR has a wavelength of 750-2500 nm. However, the absorbance bands of the NIR spectrum are often broad, non-specific, and overlapping. NIR spectrum analysis requires a multivariate method which is very subjective to noise arising from instrumentation. There is no standard protocol in modeling for classification and prediction using NIR spectra. Several models have been developed with and without pre-processing techniques. The SMOTE technique can improve the model to predict all class responses accurately. This research contributes to creating a multiclass classification model for grouping mango cultivars by finding the best pre-processing technique and using SMOTE oversampling. The results of the four test scenarios on the model's performance built using the Support Vector Machine (SVM) that the best model is obtained using spectral transformations with LSNV and CLIP operations with 100% accuracy, precision, and recall values. The Decision Tree (DT) has the performance results in 100% model was obtained by using spectral transformation with LSNV, CLIP and SAVGOL operations with parameters {'deriv_order': 0,1, 2, 'filter_win': 11, 13, 'poly_order': 3}. Using SMOTE has better accuracy than without pre-processing, with an accuracy of 92% on SVM and 94% on DT. In comparison, the combination of SMOTE and Spectral Transformation gives classification results for SVM and DT with the same accuracy of 96%, better than using SMOTE only.

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Non-destructive Measurement of Lycopene Content in High Soluble Solids Stored Tomato (Solanum Lycopersicum Mill. cv Rinka 409)

Cited by 2 publications

References 29 publications

Determination of Total Carotene and Vitamin C in Chili Powder (Capsicum annuum L.) Non-destructively Using Near-Infrared Spectroscopy

Determination of Total Carotene and Vitamin C in Chili Powder (Capsicum annuum L.) Non-destructively Using Near-Infrared Spectroscopy

Effects of Oversampling Smote and Spectral Transformations in the Classification of Mango Cultivars Using Near-Infrared Spectroscopy

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