Prediction of <i>α</i>-Solanine and <i>α</i>-Chaconine in Potato Tubers from Hunter Color Values and VIS/NIR Spectra

Tilahun, Shimeles; An, Hee Sung; Hwang, In Geun; Choi, Jong Hang; Baek, Min Woo; Choi, Han Ryul; Park, Do Su; Jeong, Cheon Soon

doi:https://doi.org/10.1155/2020/8884219

Journal of Food Quality

On this page

Abstract Introduction Materials and Methods Results and Discussion Conclusions Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

Special Issue

Methods for Determining Fruit Quality in Horticultural Crops

View this Special Issue

Research Article | Open Access

Volume 2020 | Article ID 8884219 | https://doi.org/10.1155/2020/8884219

Prediction of α-Solanine and α-Chaconine in Potato Tubers from Hunter Color Values and VIS/NIR Spectra

Shimeles Tilahun,^1,2,3Hee Sung An,¹In Geun Hwang,⁴Jong Hang Choi,¹Min Woo Baek,¹Han Ryul Choi,¹Do Su Park,⁵and Cheon Soon Jeong¹

Academic Editor: Nesibe E. Kafkas

Received28 Jul 2020

Revised09 Sept 2020

Accepted23 Oct 2020

Published17 Nov 2020

Abstract

The glycoalkaloids contents of potato tubers are usually measured by the destructive analysis that consumes time and requires expensive high-performance equipment. This study was carried out to determine the possibility of nondestructive estimation of α-solanine and α-chaconine content in potato tubers. Visible/near-infrared (VIS/NIR) spectra, color values, and the reference α-solanine and α-chaconine were measured from 180 tubers of ‘Atlantic’ and ‘Trent’ potato cultivars with eight replications at two-week intervals during the storage up to ten weeks. The partial least square (PLS) regression method was used to develop models correlating color and spectra data to the measured reference data. Regression coefficient (r) between color variables (Hunter , , and ()²) and the actual measured values of a-solanine and a-chaconine content were 0.74, 0.62, and 0.62 and 0.70, 0.58, and 0.57, respectively, for the prediction set. Concurrently, equations were developed from color variables in multiple regression with r-values of 0.76 and 0.71 for α-solanine and α-chaconine, respectively. Additionally, the selected PLS model of VIS/NIR spectra had promising predictive power for α-solanine and α-chaconine with r-values of 0.68 and 0.63, respectively, between measured and predicted samples. Taken together, although it requires further studies to improve the prediction power of the developed models, the results of this study revealed the possibility of using VIS/NIR spectra and color variables for the prediction of α-solanine and α-chaconine contents from intact unpeeled potato tubers with chemical-free, fast, and cheap assessment methods.

1. Introduction

Potato (Solanum tuberosum L.) is a starchy tuberous crop that belongs to the Solanaceae family. Potato cultivars are grown worldwide with highly diverse tuber shapes and colors [1, 2]. Potato is the world’s fourth-largest important food crop following maize (corn), wheat, and rice [3] with a worldwide production of 376.82 million tons from 19.2 million ha valued at $111.06 billion. According to FAOSTAT [3], potato production in the Republic of Korea was 631,596 tons valued at $121.66 million from 24,041 ha in 2016. Sustainable production of potato ensures long-term food security due to its high productivity and generation of more food per unit area and per unit time than maize, rice, and wheat [4]. Potato tubers are rich sources of energy due to their starch content (60–80% of the dry matter). Besides, tubers are also rich in potassium, calcium, vitamin C, and protein with good amino acid balance [5].

However, potatoes are more susceptible to quality degradation by water loss, diseases, or build-up of defense molecules due to the hydrated and metabolically active cells in tubers as compared to dry-stored crops [6]. The abundant group of defense molecules that help potatoes in deterring pests and diseases are glycoalkaloids (GAs) [6–9], yet their toxicity and bitterness are detrimental to the quality of the product. Glycoalkaloids are a class of nitrogen-containing steroidal glycosides that usually found in the genus Solanum; 95% of the total glycoalkaloids content in Solanum tuberosum primarily consists of trisaccharide steroidal glycoalkaloids α-chaconine and α-solanine [10, 11]. Total glycoalkaloid levels are highly variable in different potato cultivars and are influenced by postharvest factors such as light, mechanical injury, and storage [2, 6]. Greening occurs with an associated increase in the amount of glycoalkaloid when potato tubers are exposed to light [12]. Potato tubers that contain over 200 mg kg⁻¹ total glycoalkaloids of fresh tuber weight possess a bitter off-flavor and may cause gastroenteric symptoms, coma, and even death [2, 9, 13–15]. The toxicity of glycoalkaloids could be related to their anticholinesterase activity and disruption of cell membranes, producing neurological disorders and gastrointestinal disturbances, respectively [16]. According to Friedman and McDonald [17], the estimated highest safe level of total glycoalkaloids for human consumption is about 1 mg kg⁻¹ body weight; a level that may cause acute toxicity and a lethal dose is 1.75 and 3–6 mg kg⁻¹ body weight, respectively.

Although glycoalkaloids are perceived as potentially toxic, studies suggest that they may also possess anticarcinogenic effects, depending on the dose and conditions of use [2]. Friedman et al. [18] and Friedman [19] reported the concentration-dependent anticarcinogenic effects of α-chaconine and α-solanine against human cancer cells. Therefore, it is necessary to establish guidelines limiting the glycoalkaloid content of new cultivars before releasing them for commercial use to obtain the optimum benefit without the potential toxicity to human beings [20].

The common methods used to determine the secondary metabolites of potatoes are destructive and time-consuming and require high-performance laboratory equipment. The development of a rapid, low-cost, reliable, and reproducible analytical method that avoids the extensive sample preparation is inevitable. Pasquini [21] reported that qualitative and quantitative information can be derived from NIR range spectra from the interaction between the spectra and organic compounds that form the substance. NIR in the wavelength range between 700 nm to 110 nm could be used to determine the carbohydrate content [22] and sugar content [23] of potatoes. López-Maestresalas et al. [24] also reported nondestructive detection of black spots in potatoes by VIS/NIR (400–100 nm). The sprouting capacity of potatoes was also predicted by using NIR spectroscopy in intact potato tubers [25]. However, the prediction of glycoalkaloids from color variables and VIS/NIR spectra of intact potato tubers has not yet been reported. The objective of this study was, therefore, to develop models suitable to predict the mass fraction of α-solanine and α-chaconine content of intact potato tubers based on VIS/NIR spectra and color variables.

2. Materials and Methods

The graphical abstract summarized the overall experimental processes and contents of this article.

2.1. Plant Materials

‘Atlantic’ and ‘Trent’ potato cultivars were obtained from Haitai-Calbee snack factory, Korea. Each potato tuber was selected carefully for its freedom from defects, and relatively uniform size potato tubers were then subsequently stored at room conditions (22°C, 12-hour shift of light-dark cycles) to simulate the consumers’ practice and to allow greening. Subsampling was done at every 2-week interval and continued up to the 10th week of storage. Samples for reference analysis (α-solanine and α-chaconine) were prepared after taking VIS/NIR spectra data and color reading from intact tubers. Samples for reference analysis were frozen by liquid nitrogen and stored in a deep freezer (−80°C) until analysis [26].

2.2. Color Measurement and Analysis

Hunter , , and color variables were determined using a CR-400 chroma meter (Minolta, Tokyo, Japan). Hunter value indicates chromatic redness and it ranges from red (+ values) to green (− values), shows yellowness chromatic parameter that ranges from yellow (+ values) to blue (− values), and is the lightness parameter that indicates the degree of lightness of the sample that ranges from 0 (black) to 100 (white) [27]. Color variables were measured eight times from the surface of each tuber and the average was determined. Measurements were taken during the storage period until the 10th week at a 2-week interval. Thirty tuber samples (‘Atlantic’ and ‘Trent’; 15 each) were used during each sampling day. The tuber samples from the first and second sampling dates (60 tubers) were used for the prediction set and tuber samples from the last four consecutive sampling dates (120 tubers) were used for the prediction set. A total of 180 tuber samples were used for the experiment.

2.3. VIS/NIR Spectra Measurement and Analysis

The transmittance spectra were acquired from the intact tuber in the spectral region of 500–1100 nm with three (12 V/100 W) halogens lamp as a source of VIS/NIR light by using VIS/NIR spectrometer (Life & Tech, Co., Ltd., Yongin, Korea) (Figure 1(a)) as indicated by Tilahun et al. [26]. A tuber holder was used to keeping the tuber right above the detector (Figure 1(b)). The integration time was set to 100 ms and the measurement was done 8 times at different tuber directions per a tuber to reduce noise from being included. A total of 3500 data were saved for each measurement at 0.2 nm spectrum resolution. NIR spectrometer was connected to a computer for data transmission. A total of 1440 spectra readings were obtained from tubers throughout the storage period. Outliers were excluded and a total of 1100 spectra were used for analysis (Figure 2). Half of the samples (550 spectra readings) were used for the calibration set, and the remaining half (550 spectra readings) were used for the prediction set. Transformation of the original spectra was done by the Hanning window, standard normal variate (SNV), multiplicative scattering correction (MSC), and first derivatives to reduce systematic noise and remove unwanted information. The prediction was performed based on the lowest predicted residual error sum of squares (PRESS) value to select the optimal number of latent variables in the PLS model. Partial least square (PLS) regression analysis was performed with MATLAB R2012b (version 8.0.0.783, The Math Works, Inc., Natick, MA, USA) to establish a linear relationship between spectral data and measured references. RMSECV (root mean square of standard error in cross-validation), RMSEP (root mean square of standard error in prediction), and coefficient of determination for calibration (R²) and prediction (r) were used to evaluate the performance of the developed PLS models. A predictive model with few bias values and lower RMSECV/P is considered to be a good prediction model.

(a)

(b)

2.4. Extraction and Quantification of Glycoalkaloids for Reference Analysis

The extraction of glycoalkaloids was done from fifteen tubers for each potato cultivar at two-week interval during the 10-week storage period. Peeling of the sample tubers was not done as glycoalkaloids are mainly found in the potato skin or close to the skin [28, 29]. Extraction of 0.5 g of homogenized sample was made as stated by Tilahun et al. [29] and glycoalkaloids were analyzed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) [30]. The spectrometer was adjusted as described by Zywicki et al. [30], Nie et al. [31], and Tilahun et al. [29] for detection of α-solanine and α-chaconine.

3. Results and Discussion

3.1. Color Variables vs. the Measured Reference Analysis

Measurements of Hunter’s , , and were taken during the experiment. However, the PLS model for Hunter’s and values in the calibration data set had lower R² (<0.31) for both α-solanine and α-chaconine. Hence, we did not include Hunter’s and values for PLS model development in the prediction data set. Instead, the PLS model for and ()² values in the calibration data set had higher R² (>0.69) for both α-solanine and α-chaconine (Tables 1 and 2). Consequently, we included and ()² values for PLS model development for the prediction data set. Although no metabolic connection between chlorophyll and accumulation of glycoalkaloids has been established, the greening of tubers occurs along with the concomitant increase of glycoalkaloids [12]. Therefore, a measurement that encompasses Hunter’s value could be a good indicator of glycoalkaloids content as it indicates chromatic values that range from red to green [27]. Tilahun et al. [26] also used the same color variables (, , ()²) to predict carotenoids in intact tomato fruit.

Tables 1 and 2 show the means and ranges of reference (measured) α-solanine and α-chaconine obtained by the destructive analysis in the calibration and prediction data sets. Meanwhile, α-solanine and α-chaconine that are estimated by using color variables in the calibration and prediction data sets are also presented in Tables 1 and 2. For α-solanine, R², RMSECV, and RPD values of the calibration data set ranged between 0.74–0.85, 11.24–13.97, and 2.09–2.60, respectively. In the prediction data set, the corresponding values were 0.62–0.76, 9.09–13.54, and 0.77–1.14, respectively, for r, RMSEP, and RPD (Table 1). The highest R² was found for multivariate PLS model (R² = 0.85), followed by Hunter’s ()² (R² = 0.82), Hunter’s (0.78), and Hunter’s (R² = 0.74) in the calibration data set. For the prediction data set, the PLS models for Hunter’s and ()² had the lowest coefficient of correlation (r = 0.62), followed by Hunter’s (r = 0.74) and multivariate PLS model (r = 0.76) (Table 1 and Figure 3). The RMSECV values for Hunter’s , , ()² and multivariate PLS models were 13.97, 13.09, 12.34, and 11.24, respectively (Table 1 and Figure 3). The highest RPD value of a calibration data set was obtained for a multivariate PLS model (2.60) followed by Hunter’s ()² (2.37), (2.24), and (2.09), respectively (Table 1).

(a)

(b)

(c)

(d)

The statistics for α-chaconine also showed similar trends with α-solanine and the values for R², RMSECV, and RPD in the calibration data set were ranged between 0.69 –0.78, 7.34–8.42, and 1.85–2.12, respectively. In the prediction data set, the values for r, RMSEP, and RPD were ranged between 0.57–0.71, 6.30–8.66, and 0.49–0.67, respectively (Table 2). Interestingly, the highest R² (0.78) and r (0.71), lowest RMSEC (7.34) and RMSEP (6.30), and the highest RPD (2.12) and (0.67) values were found in calibration and prediction data sets, respectively, with multivariate PLS model (Table 2 and Figure 4).

(a)

(b)

(c)

(d)

Following the predictive analysis in multiple regression, Hunter’s , , and ()² values were found to have high predictive p-values in the prediction of both α-solanine and α-chaconine from color variables. The following equations were found to be the best equations:

The measured reference vs. predicted scores of both α-solanine and α-chaconine in the calibration and prediction sets with multivariate PLS models had shown a promising result to use the model. For the prediction data set, a multivariate PLS model had the highest coefficient of correlation (0.76) for α-solanine and (0.71) for α-chaconine (Tables 1 and 2 and Figures 1 and 2). However, it could not be claimed that this technique can be adopted with all potato cultivars as the cultivars used in the present study have white-colored tubers. Hence, further studies are needed on various cultivars having different colored tubers to develop more robust models.

3.2. VIS/NIR Spectra vs. the Measured Reference Analysis

The current demand for quality products relies on the adoption of environmentally friendly nondestructive technologies like VIS/NIR spectroscopy [4, 26] and it has gained broad acceptance for food quality evaluation [32]. VIS/NIR spectra have been reported as rapid, low-cost, and reliable method for estimation of lycopene and β-carotene in tomato [26, 33]. Bonierbale et al. [34] also reported the estimation of total and individual carotenoid concentrations in Solanum phureja cultivated potatoes by NIR spectroscopy. In this study, the transmittance energy spectra of intact potato tubers were recorded in the wavelength of 500–100 nm as shown in Figure 2. PLS models were also developed to predict α-solanine and α-chaconine based on VIS/NIR spectra of intact potato tubers and promising results were recorded. R² and RMSEC for measured vs. VIS/NIR values of α-solanine in the calibration set were 0.69 and 7.87, respectively (Figure 5(a)). Meanwhile, R² and RMSEP for reference vs. VIS/NIR values of α-solanine in the prediction set were 0.68 and 7.93, respectively (Figure 5(b)). On the other hand, R² and RMSEC for measured vs. VIS/NIR values of α-chaconine in the calibration set were 0.64 and 3.94, respectively (Figure 6(a)), while R² and RMSEP for reference vs. VIS/NIR values of α-chaconine in the prediction set were 0.63 and 3.97, respectively (Figure 6(b)). Several efforts have been made to predict different physicochemical properties of potato tubers by using VIS/NIR spectroscopy. For instance, NIR spectra were used to predict the sprouting capacity [25] and internal defects [35, 36] of tubers. Also, different infrared-based researches were reported on the prediction of quality-related parameters like dry matter content [37, 38], carbohydrate [22], and sugar content [23, 39]. Similarly, Haase [40] reported the NIR reflectance-based prediction of overall processing related quality parameters from ground raw tubers. The sensory texture of cooked potatoes was also estimated by the use of NIR spectroscopy [41, 42]. Although it requires further studies to improve the prediction power of the developed models, the results of this study revealed the possibility of using VIS/NIR spectra for the prediction of α-solanine and α-chaconine from intact unpeeled potato tubers.

(a)

(b)

(a)

(b)

4. Conclusions

The present study indicates the attempts made to predict α-solanine and α-chaconine in intact unpeeled potato tubers with chemical-free, fast, and cheap assessment methods. Models were developed by using Hunter color values and VIS/NIR spectra. Prediction of α-solanine was relatively better than α-chaconine with both color and VIS/NIR-based techniques. Our models could be a promising alternative to the costly and time-consuming destructive analysis for breeders during cultivars screening before releasing them for production. The developed models could be used easily in the field with the use of portable chroma meter and in the agricultural processing centers to sort tubers on a conveyor belt with the use of a VIS/NIR spectrometer. However, it could not be claimed that the developed models can be adopted with all potato cultivars as the cultivars used in the present study have white-colored tubers. Hence, the developed models need to be tested further on independent data sets from white-colored potato cultivars, and further studies are needed on various cultivars having different colored tubers to develop more robust nondestructive methods for the estimation of the glycoalkaloids content of the intact potato tubers.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Authors’ Contributions

S.T., H.S.A., and C.S.J. contributed to conceptualization. S.T. and H.S.A. performed methodology. S.T., J.H.C., H.R.C., and M.W.B. executed experiment. S.T. and I.G.H. were responsible for software. Formal analysis was performed by S.T. and I.G.H. C.S.J. contributed to resources. Original draft was prepared by S.T. Review and editing were carried out by D.S.P. and C.S.J. C.S.J. contributed to supervision. Project administration was carried out by M.W.B. Funding was acquired by C.S.J. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

Financial support from scientific and technological support program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019K1A3A9A0100002412) is gratefully acknowledged.

Supplementary Materials

Graphical abstract is provided in this section. (Supplementary Materials)

References

R. Machida-Hirano, “Diversity of potato genetic resources,” Breeding Science, vol. 65, no. 1, pp. 26–40, 2015.
View at: Publisher Site | Google Scholar
M. Friedman, “Potato glycoalkaloids and metabolites: roles in the plant and in the diet,” Journal of Agricultural and Food Chemistry, vol. 54, no. 23, pp. 8655–8681, 2006.
View at: Publisher Site | Google Scholar
FAOSTAT Food & Agriculture Organization of the United Nations Statistics Division, 2017.
A. López, S. Arazuri, I. García, J. Mangado, and C. Jarén, “A review of the application of near-infrared spectroscopy for the analysis of potatoes,” Journal of Agricultural and Food Chemistry, vol. 61, no. 23, pp. 5413–5424, 2013.
View at: Publisher Site | Google Scholar
S. Prokop and J. Albert, “Potatoes , nutrition and diet,” in Proceedings of the International Year of the Potato 2008, vol. 1–2, Rome, Italy, November 2008.
View at: Google Scholar
A. Kjær, G. Nielsen, S. Stærke, M. R. Clausen, M. Edelenbos, and B. Jørgensen, “Detection of glycoalkaloids and chlorophyll in potatoes (Solanum tuberosum L.) by hyperspectral imaging,” American Journal of Potato Research, vol. 94, pp. 573–582, 2017.
View at: Publisher Site | Google Scholar
S. Chowański, Z. Adamski, P. Marciniak et al., “A review of bioinsecticidal activity of Solanaceae alkaloids,” Toxins, vol. 8, no. 3, 60 pages, 2016.
View at: Publisher Site | Google Scholar
A. F. Sánchez-Maldonado, A. Schieber, and M. G. Gänzle, “Antifungal activity of secondary plant metabolites from potatoes (Solanum tuberosumL.): glycoalkaloids and phenolic acids show synergistic effects,” Journal of Applied Microbiology, vol. 120, no. 4, pp. 955–965, 2016.
View at: Publisher Site | Google Scholar
M. Cantwell, “A review of important facts about potato glycoalkaloids,” Perishables Handling Newsletter, vol. 87, 27 pages, 1996.
View at: Google Scholar
A. K. K. Arslan and M. B. Yerer, “α-Chaconine and α-Solanine inhibit RL95-2 endometrium cancer cell proliferation by reducing expression of Akt (Ser473) and ERα (Ser167),” Nutrients, vol. 10, no. 6, 672 pages, 2018.
View at: Publisher Site | Google Scholar
K.-H. Shen, A. Liao, J.-H. Hung et al., “α-Solanine inhibits invasion of human prostate cancer cell by suppressing epithelial-mesenchymal transition and MMPs expression,” Molecules, vol. 19, no. 8, pp. 11896–11914, 2014.
View at: Publisher Site | Google Scholar
L. A. Grunenfelder, L. O. Knowles, L. K. Hiller, and N. R. Knowles, “Glycoalkaloid development during greening of fresh market potatoes (solanum tuberosumL.),” Journal of Agricultural and Food Chemistry, vol. 54, no. 16, pp. 5847–5854, 2006.
View at: Publisher Site | Google Scholar
H. Kodamatani, K. Saito, N. Niina, S. Yamazaki, and Y. Tanaka, “Simple and sensitive method for determination of glycoalkaloids in potato tubers by high-performance liquid chromatography with chemiluminescence detection,” Journal of Chromatography A, vol. 1100, no. 1, pp. 26–31, 2005.
View at: Publisher Site | Google Scholar
A. Sotelo and B. Serrano, “High-performance liquid chromatographic determination of the glycoalkaloids α-solanine and α-chaconine in 12 commercial varieties of Mexican potato,” Journal of Agricultural and Food Chemistry, vol. 48, no. 6, pp. 2472–2475, 2000.
View at: Publisher Site | Google Scholar
D. T. Coxon, K. R. Price, and P. G. Jones, “A simplified method for the determination of total glycoalkaloids in potato tubers,” Journal of the Science of Food and Agriculture, vol. 30, no. 11, pp. 1043–1049, 1979.
View at: Publisher Site | Google Scholar
S. E. Milner, N. P. Brunton, P. W. Jones, N. M. O’ Brien, S. G. Collins, and A. R. Maguire, “Bioactivities of glycoalkaloids and their aglycones from solanum species,” Journal of Agricultural and Food Chemistry, vol. 59, no. 8, pp. 3454–3484, 2011.
View at: Publisher Site | Google Scholar
K. Brinsona, P. M. Dey, M. A. John, and J. B. Pridham, “Post-harvest changes in mangifera mesocarp cell walls and cytoplasmic polysaccharides,” Phytochemistry, vol. 27, pp. 719–723, 1988.
View at: Publisher Site | Google Scholar
M. Friedman, K.-R. Lee, H.-J. Kim, I.-S. Lee, and N. Kozukue, “Anticarcinogenic effects of glycoalkaloids from potatoes against human cervical, liver, lymphoma, and stomach cancer cells,” Journal of Agricultural and Food Chemistry, vol. 53, no. 15, pp. 6162–6169, 2005.
View at: Publisher Site | Google Scholar
M. Friedman, “Chemistry and anticarcinogenic mechanisms of glycoalkaloids produced by eggplants, potatoes, and tomatoes,” Journal of Agricultural and Food Chemistry, vol. 63, no. 13, pp. 3323–3337, 2015.
View at: Publisher Site | Google Scholar
J. Valcarcel, K. Reilly, M. Gaffney, and N. O’Brien, “Effect of genotype and environment on the glycoalkaloid content of rare, heritage, and commercial potato varieties,” Journal of Food Science, vol. 79, no. 5, pp. T1039–1049, 2014.
View at: Publisher Site | Google Scholar
C. Pasquini, “Near infrared spectroscopy: fundamentals, practical aspects and analytical applications,” Journal of the Brazilian Chemical Society, vol. 14, no. 2, pp. 198–219, 2003.
View at: Publisher Site | Google Scholar
Y. C. Jie, Y. Miao, H. Zhang, and R. Matsunaga, “Non-destructive determination of carbohydrate content in potatoes using near infrared spectroscopy,” Journal of Near Infrared Spectroscopy, vol. 12, pp. 311–314, 2004.
View at: Publisher Site | Google Scholar
J. Y. Chen, H. Zhang, Y. Miao, and M. Asakura, “Nondestructive determination of sugar content in potato tubers using visible and near infrared spectroscopy,” Japan Journal of Food Engineering, vol. 11, no. 1, pp. 59–64, 2010.
View at: Publisher Site | Google Scholar
A. López-Maestresalas, J. C. Keresztes, M. Goodarzi, S. Arazuri, C. Jarén, and W. Saeys, “Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging,” Food Control, vol. 70, pp. 229–241, 2016.
View at: Publisher Site | Google Scholar
J.-C. Jeong, H.-C. Ok, O.-S. Hur, and C.-G. Kim, “Prediction of sprouting capacity using near-infrared spectroscopy in potato tubers,” American Journal of Potato Research, vol. 85, no. 5, pp. 309–314, 2008.
View at: Publisher Site | Google Scholar
S. Tilahun, D. S. Park, M. H. Seo et al., “Prediction of lycopene and β-carotene in tomatoes by portable chroma-meter and VIS/NIR spectra,” Postharvest Biology and Technology, vol. 136, pp. 50–56, 2018.
View at: Publisher Site | Google Scholar
R. G. McGuire, “Reporting of objective color measurements,” HortScience, vol. 27, no. 12, pp. 1254-1255, 1992.
View at: Publisher Site | Google Scholar
G. K. Kirui, A. K. Misra, O. M. Olanya, M. Friedman, R. El-Bedewy, and P. T. Ewell, “Glycoalkaloid content of some superior potato (Solanum tuberosumL) clones and commercial cultivars,” Archives of Phytopathology and Plant Protection, vol. 42, no. 5, pp. 453–463, 2009.
View at: Publisher Site | Google Scholar
S. Tilahun, H. S. An, H. R. Choi, D. S. Park, J. S. Lee, and C. S. Jeong, “Effects of storage temperature on glycoalkaloid content , acrylamide formation , and processing-related variables of potato cultivars,” Journal of Agriculture and Environmental Sciences, vol. 31, pp. 128–142, 2019.
View at: Publisher Site | Google Scholar
B. Zywicki, G. Catchpole, J. Draper, and O. Fiehn, “Comparison of rapid liquid chromatography-electrospray ionization-tandem mass spectrometry methods for determination of glycoalkaloids in transgenic field-grown potatoes,” Analytical Biochemistry, vol. 336, no. 2, pp. 178–186, 2005.
View at: Publisher Site | Google Scholar
X. Nie, G. Zhang, S. Lv, and H. Guo, “Steroidal glycoalkaloids in potato foods as affected by cooking methods,” International Journal of Food Properties, vol. 21, no. 1, pp. 1875–1887, 2018.
View at: Publisher Site | Google Scholar
L. E. Rodriguez-Saona and M. E. Allendorf, “Use of FTIR for rapid authentication and detection of adulteration of food,” Annual Review of Food Science and Technology, vol. 2, no. 1, pp. 467–483, 2011.
View at: Publisher Site | Google Scholar
A. Clément, M. Dorais, and M. Vernon, “Nondestructive measurement of fresh tomato lycopene content and other physicochemical characteristics using visible NIR spectroscopy,” Journal of Agricultural and Food Chemistry, vol. 56, pp. 9813–9818, 2008.
View at: Publisher Site | Google Scholar
M. Bonierbale, W. Grüneberg, W. Amoros et al., “Total and individual carotenoid profiles in Solanum phureja cultivated potatoes: II. Development and application of near-infrared reflectance spectroscopy (NIRS) calibrations for germplasm characterization,” Journal of Food Composition and Analysis, vol. 22, no. 6, pp. 509–516, 2009.
View at: Publisher Site | Google Scholar
Z. Zhou, S. Zeng, X. Li, and J. Zheng, “Nondestructive detection of blackheart in potato by visible/near infrared transmittance spectroscopy,” Journal of Spectroscopy, vol. 2015, Article ID 786709, 9 pages, 2015.
View at: Publisher Site | Google Scholar
E. K. Kemsley, H. S. Tapp, R. Binns, R. O. Mackin, and A. J. Peyton, “Feasibility study of NIR diffuse optical tomography on agricultural produce,” Postharvest Biology and Technology, vol. 48, no. 2, pp. 223–230, 2008.
View at: Publisher Site | Google Scholar
P. P. Subedi and K. B. Walsh, “Assessment of potato dry matter concentration using short-wave near-infrared spectroscopy,” Potato Research, vol. 52, no. 1, pp. 67–77, 2009.
View at: Publisher Site | Google Scholar
T. Helgerud, J. P. Wold, M. B. Pedersen et al., “Towards on-line prediction of dry matter content in whole unpeeled potatoes using near-infrared spectroscopy,” Talanta, vol. 143, pp. 138–144, 2015.
View at: Publisher Site | Google Scholar
A. M. Rady and D. E. Guyer, “Evaluation of sugar content in potatoes using NIR reflectance and wavelength selection techniques,” Postharvest Biology and Technology, vol. 103, pp. 17–26, 2015.
View at: Publisher Site | Google Scholar
N. U. Haase, “Prediction of potato processing quality by near infrared reflectance spectroscopy of ground raw tubers,” Journal of Near Infrared Spectroscopy, vol. 19, no. 1, pp. 37–45, 2011.
View at: Publisher Site | Google Scholar
A. K. Thybo, I. E. Bechmann, M. Martens, and S. B. Engelsen, “Prediction of sensory texture of cooked potatoes using uniaxial compression, near infrared spectroscopy and low Field1H NMR spectroscopy,” LWT–Food Science and Technology, vol. 33, no. 2, pp. 103–111, 2000.
View at: Publisher Site | Google Scholar
C. Van Dijk, M. Fischer, J. Holm, J.-G. Beekhuizen, T. Stolle-Smits, and C. Boeriu, “Texture of cooked potatoes (Solanum tuberosum). 1. Relationships between dry matter content, sensory-perceived texture, and near-infrared spectroscopy,” Journal of Agricultural and Food Chemistry, vol. 50, no. 18, pp. 5082–5088, 2002.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2020 Shimeles Tilahun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

1619

Downloads

967

Citations