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    Evaluation of informative spectral wavelengths for estimating soluble solids content in sugarcane billets
    (2022-02-22)
    Udompetaikul, Vasu
    ;
    Phetpan, Kittisak
    ;
    Sirisomboon, Panmanas
    This study proposed individual spectral wavelengths significant to estimate soluble solids content (SSC) in sugarcane billets moving on the conveyor. At the same time, an all-in-one quality and yield monitor using those wavelengths for a sugarcane harvester was also proposed. Seven wavelengths, 475, 560, 668, 717, 755, 840, and 890 nm, were arranged into three groups for modeling. Group 1, consisting of 475, 560, 668, 717, and 840 nm, was based on the spectral responses of a commercial multispectral camera, while group 2 (717 and 840 nm) was based on the invisible (RedEdge and near-infrared or NIR) responses of the camera. For group 3, two sugar-related wavelengths at 755 and 890 nm were selected as the candidates for modeling. Partial least squares regression (PLSR) was employed to model those three groups with corresponding soluble solids content (SSC). The results showed that the developed models based on two sugar-related wavelengths at 755 and 890 nm provided the best performance, explaining 80.2 % of the variance in the SSC and displaying a root mean square error of calibration (RMSEC) of 0.32 ºBrix. The predictive performance had the root mean square error of prediction (RMSEP) of 0.33 ºBrix. This finding confirmed the effectiveness of the sugar wavelengths and conveyed the possibility to develop the sugarcane quality and yield monitor.
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    Evaluation of soluble solids of curry soup containing coconut milk by near infrared spectroscopy
    (2017-06-01)
    Sirisomboon, Panmanas
    ;
    Nawayon, Jutarat
    The aim of this research was to do a feasibility study of near infrared spectroscopy to evaluate soluble solids of curry soup containing coconut milk. The soup samples were collected from mixing tanks, water adjusting tanks, an ultra-high temperature process line and laminated cartons. There were also soluble solids adjusted samples by adding or reducing coconut sugar where the curry was made from the same recipe as in the processing line but increasing 30, 60 and 90% coconut sugar and reducing 30, 60 and 90% coconut sugar from normal. There were 119 samples in total. Sample was scanned with an FT-NIR spectrometer. A prediction model for soluble solids was established using near infrared spectral data in conjunction with partial least squares regression. When validated using a set of test samples, the model developed using spectra pretreated by min-max normalization in the range 9403.8–6094.3 cm<sup>-1</sup>, provided a coefficient of determination (r<sup>2</sup>), root mean square error of prediction, bias and ratio of performance to interquartile of 0.92, 1.0°Brix, 0.1°Brix and 2.4, respectively. It showed the potential of using near infrared spectroscopy to evaluate soluble solids in curry soup. With further development using more natural samples, a more robust model could be achieved to evaluate soluble solids in curry soup in a processing factory.
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    Determination of dry matter and soluble solids of durian pulp using diffuse reflectance near infrared spectroscopy
    (2015-01-01)
    Onsawai, Phalanon
    ;
    Sirisomboon, Panmanas
    Fourier transform near infrared spectroscopy was used as a non-invasive technique for the determination of dry matter and soluble solids of durian pulp. A set of 25 fruit was randomly harvested every 10 days, starting from 80 days until 127 days after the onset of fruit development covering six levels of maturity (80 days, 90 days, 100 days, 110 days, 120 days and 127 days). After applying ethephon on the fruit stems, the fruits were kept for 3 days at room temperature and allowed to ripen. Only the pulp of the durian was scanned. The dry matter and soluble solids reference values of the samples were determined by a hot-air-oven method and using a refractometer, respectively. Prediction models using half the samples related the spectral data, and dry matter and soluble solids data were subsequently established using partial least-squares regression and validated using the other half of the samples in a prediction set. A full cross-validation was also generated using all 149 samples. Both the half-of-the-samples model and the all-sample model were then validated using a true validation set of samples collected in a later year. When tested against the validation half of the samples, the halfof- the-samples model predicted dry-matter content with a coefficient of determination (r2) and root mean square error of prediction (RMSEP) of 0.89 and 3.60%, respectively, and for soluble solids content 0.55 and 1.63 °Brix (Bx), respectively. When tested on samples from a later season, the model for dry-matter content returned an r2, RMSEP and bias of 0.26, 6.10% and 2.16%, respectively, and for soluble solids content 0.27, 1.25 °Bx and 1.09 °Bx, respectively. The cross-validated model for dry matter yielded a slightly better r2 and root mean square error of cross-validation (RMSECV) of 0.90 and 3.58%, respectively, however, the model for soluble solids did not provide a better r2 and RMSECV: 0.51 and 1.81 °Bx, respectively. When tested on samples from a later season, the cross-validated models gave, r2, RMSEP and bias of 0.15, 5.17% and 1.49%, respectively, for dry-matter content, and for soluble solids content 0.37, 1.32 °Bx and 1.23 °Bx, respectively. The poor results obtained when predicting dry matter in samples in later seasons indicate that samples from several seasons must be included in the set of calibration samples. This is the first report on the application of NIR spectroscopy to evaluate the dry matter and soluble solids of durian pulp and could be useful to customers, exporters, importers and also postharvest technologists. However, prediction accuracy was not demonstrated in the model for durian pulp soluble solids, possibly because of the effect of ethephon applied after harvesting to induce ripening within 3 days to make the fruit suitable for consumption. In addition, it was found that the vibration bands of cellulose and fat, and those of aromatic, CH2 and sucrose highly affected the predictions of dry matter and soluble solids in the durian pulp, respectively.
      2
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    Change in fruit quality and cell wall polysaccharides in bruised papaya cultivars 'Khak dum' and 'Holland'
    (2012-04-01)
    Khurnpoon, L.
    ;
    Siriphanich, J.
    Papaya bruising is a major postharvest problem that substantially reduces fruit quality. During the process of ripening, Thai papaya 'Khak dum' is softer than imported cultivar 'Holland' (Pak Mai Lai). 'Khak dum' is more susceptible to bruising than 'Holland'. Due to lack of detailed knowledge about bruising mechanisms, effective bruise prevention is difficult. Pulp firmness at bruised site were approximately 60 and 30 N before impact then rapidly dropped to about 30 and 20 N after 2 days following impact, while it dropped to about 45 and 28 N in control for 'Khak dum' and 'Holland', respectively. The bruised tissues expanded, became darker in color to larger bruise areas of 20.42 and 24.09 cm2, respectively and decayed 2 and 4 days following impact. Respiration rate also increased by 1.5-times in 24 h following impact then declined. Soluble solids content was not significantly different between bruised and control fruit, but slightly increased by the end of storage. Pulp softening at the bruise area was associated with the increase in water soluble pectin fraction approximately 3 and 1.5 times as compared to the control in 'Khak dum' and 'Holland', respectively, while CDTA and 4 M KOH soluble fractions slightly decreased by the end of storage. Na2CO3 slightly increased in 'Khak dum' while it decreased in 'Holland'.
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    Evaluation of artificial neural networks for pineapple grading
    (2006-10-01)
    Boonmung, Suwanee
    ;
    Chomtee, Boonorm
    ;
    Kanlayasiri, Kannachai
    The objective of this study is to evaluate resonant frequency, firmness and soluble solids for pineapple classification using artificial neural networks (ANNs) as the analytical tool. A sample of 149 pineapples was classified based on their internal qualities into five classes: unripe, partially ripe, ripe, partially overripe and completely overripe. The developed ANN model successfully classified pineapples into merely three classes as unripe, ripe and completely overripe. The most effective model was obtained when both resonant frequency and soluble solids were included in the model. The classification accuracy was more than 83% for all three classes. © 2006, Blackwell Publishing.
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