Prediction of optimal cooking time for boiled potatoes by hyperspectral imaging

Trong, Nghia Nguyen Do; Tsuta, Mizuki; Nicolaı̈, Bart; Baerdemaeker, Josse De; Saeys, Wouter

doi:10.1016/j.jfoodeng.2011.03.031

Cited by 66 publications

(19 citation statements)

References 27 publications

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“…Water treated by reverse osmosis and carbon filtration was used to rinse and cook samples as needed. The use of ultra-purified water to help prevent additional substances present in water from affecting the properties of the final samples was suggested by Nguyen do Trong et al [20]. The skin of each potato was removed using a vegetable peeler, then each potato was rinsed under deionized water to remove any remaining dirt.…”

Section: Sample Preparationmentioning

confidence: 99%

Determining Which Cooking Method Provides the Best Sensory Differentiation of Potatoes

Ciccone¹,

Chambers²,

Chambers³

et al. 2020

Foods

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There are many ways to prepare potatoes that each provide a unique set of sensory properties. However, when conducting a descriptive sensory study, it is important to utilize a cooking method that will highlight, and not distract from, the sensory differences among potato samples due to factors such as variety or growing conditions. This study aimed to determine which of five cooking methods results in the best differentiation among potato varieties to recommend a single method for use in future descriptive sensory studies. Five different potato varieties were each prepared using boiling, mashing, baking, frying, and air frying methods. The samples were provided to six highly trained descriptive panelists and evaluated by consensus using a modified high identity traits (HITs) method. Panelists evaluated the aroma, flavor, and texture to develop a list of up to five total HITs per sample. Additionally, panelists scored each sample for degree of difference (DOD) from the control. Based on the HITs profiles and DOD scores, mashing, baking, and air frying methods were all effective in differentiating the samples. Frying and boiling methods introduced too much variation and are not recommended for sample differentiation. Ultimately, the method chosen for future research would depend on the study objectives.

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Section: Sample Preparationmentioning

confidence: 99%

Determining Which Cooking Method Provides the Best Sensory Differentiation of Potatoes

Ciccone¹,

Chambers²,

Chambers³

et al. 2020

Foods

View full text Add to dashboard Cite

show abstract

“…Damage in fruit cell membranes due to chilling injury affects normal firmness, and therefore changes in firmness could be used as an indication of possible chilling injury. The starch index was also employed by Nguyen Do Trong et al (2011) to estimate the optimal cooking time of potatoes. The changes in the microstructure during cooking affect the interaction of light with the starch granules in different regions inside the potato.…”

Section: Estimation Of Internal Quality Parametersmentioning

confidence: 99%

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

Lorente

Aleixos

Gómez-Sanchís

et al. 2011

Food Bioprocess Technol

459

190

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Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time consuming for such high dimensional data sets and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, nondestructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.

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“…These studies all showed the feasibility of characterizing water content and chromaticity by HSI. As for the quality assessment of potatoes, HSI has been used in the following studies: prediction of pigment content in purple-fleshed sweet potato tuber slices [15], sugar content detection [16], determination of starch content [17], prediction of starch, soluble sugars and amino acids [18], identification of sliced organic potatoes [19], prediction of sprouting potato eyes [20], evaluation of optimal cooking time for boiled potatoes [21], classification of defective potatoes [22], as well as detection of blackspot [23]. Moreover, Sun et al [24] utilized HSI to predict the moisture content and freezable water content of purple-fleshed sweet potato slices during drying process.…”

Section: Introductionmentioning

confidence: 99%

Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis

Xiao

Bai

2020

Foods

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Color index and water content are important indicators for evaluating the quality of fresh-cut potato tuber slices. In this study, hyperspectral imaging combined with multivariate analysis was used to detect the color parameters (L*, a*, b*, Browning index (BI), L*/b*) and water content of fresh-cut potato tuber slices. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic wavelengths, partial least squares (PLS) and least squares support vector machine (LS-SVM) were utilized to establish regression models. For color prediction, R2c, R2p and RPD of all the LSSVM models established for the five color indicators L*, a*, b*, BI, L*/b* were exceeding 0.90, 0.84 and 2.1, respectively. For water content prediction, R2c, R2p, and RPD of the LSSVM models were over 0.80, 0.77 and 1.9, respectively. LS-SVM model based on full spectra was used to reappear the spatial distribution of color and water content in fresh-cut potato tuber slices by pseudo-color imaging since it performed best in most cases. The results illustrated that hyperspectral imaging could be an effective method for color and water content prediction, which could provide solid theoretical basis for subsequent grading and processing of fresh-cut potato tuber slices.

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Prediction of optimal cooking time for boiled potatoes by hyperspectral imaging

Cited by 66 publications

References 27 publications

Determining Which Cooking Method Provides the Best Sensory Differentiation of Potatoes

Determining Which Cooking Method Provides the Best Sensory Differentiation of Potatoes

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis

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