Monitoring textural changes in raw and cooked potatoes by near-infrared spectroscopy | |
| Author | Nguyen Thai Loc |
| Call Number | AIT Thesis no.FB-05-19 |
| Subject(s) | Near infrared spectroscopy Potatoes |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science, School of Environment, Resources and Development |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. FB-05-19 |
| Abstract | The ability of near-infrared reflectance (NIR) measurements to predict mechanical textural parameters of potatoes during thermal softening and storage was investigated. For thermal softening process, cylindrical potato samples, 20 mm diameter and 10 mm long, were heated in water at four temperatures 60, 70, 80, 90°C during selected durations. In the other experiment, changes in textural parameter were measured for potatoes stored at ambient temperature. Near-infrared reflectance measurement over the range 1 100 - 2500 nm was collected from the respective samples. Rupture force (F), normalized rupture force (F,/Fo), area to rupture (A), normalized area to rupture (A,/Ao), elasticity modulus were obtained from uniaxial compression test and used as reference parameters. MLR, PLSR and ANN were comparatively evaluated for the capability of analyzing near-infrared spectra. Is was found that textural parameters of potatoes during thermal softening had relatively good relationship with NIR spectra, indicated by regression coefficient ranging from 0.814 - 0.876. However, accuracy of prediction was not yet satisfactory with RMSE percent to mean of predicted values generally over 20%. Elasticity modulus of raw potatoes during storage could be predicted by LAIRS with R and RMSEP of 0.791 and 0.26 respectively. For cooked potatoes, the elasticity modulus had poor relation to NIR spectra of the corresponding raw sample. Among the methods of NIR spectral data analysis, MLR models were least robustly constructed as indicated by significant difference between results of the calibration and test set. PLSR and ANN had better modeling performance, but with respects to R and RIMSE, ANN performance was slightly improved. ANN could be used as an effective method for analysis of NIR spectra. |
| Year | 2005 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. FB-05-19 |
| Type | Thesis |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Food, Agriculture and Natural Resources (Former title: Department of Food Agriculture, and BioResources (DFAB)) |
| Academic Program/FoS | Food Engineering and Bioprocess Technology (FB) |
| Chairperson(s) | Jindal, Vinod Kumar; |
| Examination Committee(s) | Athapol Noomhorm ;Rakshit, Sudip Kumar; |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship; |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 2005 |