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Abubakar M, Wasswa P, Masumba E, Ongom P, Mkamilo G, Kanju E, Abincha W, Edema R, Sichalwe K, Tukamuhabwa P, Kayondo S, Rabbi I, Kulembeka H. Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour. Sci Rep 2024; 14:17130. [PMID: 39054362 PMCID: PMC11272776 DOI: 10.1038/s41598-024-67299-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 07/10/2024] [Indexed: 07/27/2024] Open
Abstract
Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740-1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R2, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.
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Affiliation(s)
- Mikidadi Abubakar
- Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.
| | - Peter Wasswa
- Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda
| | - Esther Masumba
- Tanzania Agricultural Research Institute (TARI), Kibaoni, Tanzania
| | - Patrick Ongom
- International Institute of Tropical Agriculture (IITA), Kano, Nigeria
| | - Geoffrey Mkamilo
- Tanzania Agricultural Research Institute (TARI), Kibaoni, Tanzania
| | - Edward Kanju
- International Institute of Tropical Agriculture (IITA), Dar es Salaam, Tanzania
| | - Wilfred Abincha
- Kenya Agricultural and Livestock Research Organization (KALRO), Kakamega, Kenya
| | - Richard Edema
- Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda
| | - Karoline Sichalwe
- Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda
| | - Phinehas Tukamuhabwa
- Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda
| | - Siraj Kayondo
- International Institute of Tropical Agriculture (IITA), Dar es Salaam, Tanzania
| | - Ismail Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
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Sousa MBE, Filho JSS, de Andrade LRB, de Oliveira EJ. Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis. FRONTIERS IN PLANT SCIENCE 2023; 14:1089759. [PMID: 36755702 PMCID: PMC9900181 DOI: 10.3389/fpls.2023.1089759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/09/2023] [Indexed: 06/18/2023]
Abstract
Cassava (Manihot esculenta Crantz) starch consists of amylopectin and amylose, with its properties determined by the proportion of these two polymers. Waxy starches contain at least 95% amylopectin. In the food industry, waxy starches are advantageous, with pastes that are more stable towards retrogradation, while high-amylose starches are used as resistant starches. This study aimed to associate near-infrared spectrophotometry (NIRS) spectra with the waxy phenotype in cassava seeds and develop an accurate classification model for indirect selection of plants. A total of 1127 F2 seeds were obtained from controlled crosses performed between 77 F1 genotypes (wild-type, Wx_). Seeds were individually identified, and spectral data were obtained via NIRS using a benchtop NIRFlex N-500 and a portable SCiO device spectrometer. Four classification models were assessed for waxy cassava genotype identification: k-nearest neighbor algorithm (KNN), C5.0 decision tree (CDT), parallel random forest (parRF), and eXtreme Gradient Boosting (XGB). Spectral data were divided between a training set (80%) and a testing set (20%). The accuracy, based on NIRFlex N-500 spectral data, ranged from 0.86 (parRF) to 0.92 (XGB). The Kappa index displayed a similar trend as the accuracy, considering the lowest value for the parRF method (0.39) and the highest value for XGB (0.71). For the SCiO device, the accuracy (0.88-0.89) was similar among the four models evaluated. However, the Kappa index was lower than that of the NIRFlex N-500, and this index ranged from 0 (parRF) to 0.16 (KNN and CDT). Therefore, despite the high accuracy these last models are incapable of correctly classifying waxy and non-waxy clones based on the SCiO device spectra. A confusion matrix was performed to demonstrate the classification model results in the testing set. For both NIRS, the models were efficient in classifying non-waxy clones, with values ranging from 96-100%. However, the NIRS differed in the potential to predict waxy genotype class. For the NIRFlex N-500, the percentage ranged from 30% (parRF) to 70% (XGB). In general, the models tended to classify waxy genotypes as non-waxy, mainly SCiO. Therefore, the use of NIRS can perform early selection of cassava seeds with a waxy phenotype.
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Nkouaya Mbanjo EG, Hershberger J, Peteti P, Agbona A, Ikpan A, Ogunpaimo K, Kayondo SI, Abioye RS, Nafiu K, Alamu EO, Adesokan M, Maziya-Dixon B, Parkes E, Kulakow P, Gore MA, Egesi C, Rabbi IY. Predicting starch content in cassava fresh roots using near-infrared spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 13:990250. [PMID: 36426140 PMCID: PMC9679500 DOI: 10.3389/fpls.2022.990250] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 09/14/2022] [Indexed: 05/20/2023]
Abstract
The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740-1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350-2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400-2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2 P): 0.84-0.90; ratio of performance deviation (RPD): 2.49-3.11, ratio of performance to interquartile distance (RPIQ): 3.24-4.08, concordance correlation coefficient (CCC): 0.91-0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871-1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.
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Affiliation(s)
- Edwige Gaby Nkouaya Mbanjo
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
- *Correspondence: Edwige Gaby Nkouaya Mbanjo,
| | - Jenna Hershberger
- Department of Plant and Environmental Sciences, Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
| | - Prasad Peteti
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Afolabi Agbona
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
- Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX, United States
| | - Andrew Ikpan
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Kayode Ogunpaimo
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Siraj Ismail Kayondo
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Racheal Smart Abioye
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Kehinde Nafiu
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | | | - Michael Adesokan
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Busie Maziya-Dixon
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Elizabeth Parkes
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Michael A. Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Chiedozie Egesi
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- National Root Crops Research Institute (NRCRI), Umuahia, Nigeria
| | - Ismail Yusuf Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
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Sankom A, Mahakarnchanakul W, Rittiron R, Sajjaanantakul T, Thongket T. Detection of Profenofos in Chinese Kale, Cabbage, and Chili Spur Pepper Using Fourier Transform Near-Infrared and Fourier Transform Mid-Infrared Spectroscopies. ACS OMEGA 2021; 6:26404-26415. [PMID: 34660998 PMCID: PMC8515571 DOI: 10.1021/acsomega.1c03674] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/10/2021] [Indexed: 06/12/2023]
Abstract
Different types of quantitative technology based on infrared spectroscopy to detect profenofos were compared based on Fourier transform near-infrared (FT-NIR; 12,500-4000 cm-1) and Fourier transform mid-infrared (FT-MIR; 4000-400 cm-1) spectroscopies. Standard solutions in the range of 0.1-100 mg/L combined with the dry-extract system for infrared (DESIR) technique were analyzed. Based on partial least-squares regression (PLSR) to develop a calibration equation, FT-NIR-PLSR produced the best prediction of profenofos residues based on the values for R 2 (0.87), standard error of prediction or SEP (11.68 mg/L), root-mean-square error of prediction or RMSEP (11.50 mg/L), bias (-0.81 mg/L), and ratio performance to deviation or RPD (2.81). In addition, FT-MIR-PLSR produced the best prediction of profenofos residues based on the values for R 2 (0.83), SEP (13.10 mg/L), RMSEP (13.00 mg/L), bias (1.46 mg/L), and RPD (2.49). Based on the ease of use and appropriate sample preparation, FT-NIR-PLSR combined with DESIR was chosen to detect profenofos in Chinese kale, cabbage, and chili spur pepper at concentrations of 0.53-106.28 mg/kg. The quick, easy, cheap, effective, rugged, and safe technique coupled with gas chromatography-mass spectrometry was used to obtain the actual values. The best FT-NIR-PLSR equation provided good profenofos detection in all vegetables based on values for R 2 (0.88-0.97), SEP (5.27-11.07 mg/kg), RMSEP (5.25-11.00 mg/kg), bias (-1.39 to 1.30 mg/kg), and RPD (2.91-5.22). These statistics revealed no significant differences between the FT-NIR predicted values and actual values at a confidence interval of 95%, with agreeable results presented at pesticide residue levels over 30 mg/kg. FT-NIR spectroscopy combined with DESIR and PLSR should be considered as a promising screening method for pesticide detection in vegetables.
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Affiliation(s)
- Atchara Sankom
- Department
of Food Science and Technology, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand
- Center
for Advanced Studies for Agriculture and Food, Kasetsart University
Institute for Advanced Studies, Kasetsart
University, Bangkok 10900, Thailand
| | - Warapa Mahakarnchanakul
- Department
of Food Science and Technology, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand
- Center
for Advanced Studies for Agriculture and Food, Kasetsart University
Institute for Advanced Studies, Kasetsart
University, Bangkok 10900, Thailand
| | - Ronnarit Rittiron
- Department
of Food Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand
| | - Tanaboon Sajjaanantakul
- Department
of Food Science and Technology, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand
| | - Thammasak Thongket
- Department
of Horticulture, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand
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Kusumiyati, Hadiwijaya Y, Putri IE, Munawar AA. Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy. Heliyon 2021; 7:e07677. [PMID: 34401571 PMCID: PMC8353486 DOI: 10.1016/j.heliyon.2021.e07677] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/14/2021] [Accepted: 07/26/2021] [Indexed: 11/26/2022] Open
Abstract
Latest studies on Vis/NIR research mostly focused on particular products. Developing a model for a specific product is costly and laborious. This study utilized visible/near-infrared (Vis/NIR) spectroscopy to evaluate the quality attributes of six products of the Cucurbitaceae family, with a single estimation model, rather than individually. The study made use of six intact products, zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber. Subsequently, the multi-product models for soluble solids content (SSC) and water content were created using partial least squares regression (PLSR) method. The PLSR modeling produced satisfactory results, the coefficient of determination in calibration set (R2c) was discovered to be 0.95 and 0.92, while the root mean squares error of calibration (RMSEC) was found to be 0.41 and 0.61, for SSC and water content, respectively. These models were able to accurately predict the unknown samples with coefficient of determination in prediction set (R2p) of 0.96 and 0.92, as well as root mean squares error of prediction (RMSEP) of 0.32 and 0.58, while the ratio of prediction to deviation (RPD) was found to be 5.68 and 3.69 for SSC and water content, respectively. This shows Vis/NIR spectroscopy was able to quantify the SSC and water content of six products of Cucurbitaceae family, using a single model.
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Affiliation(s)
- Kusumiyati
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Yuda Hadiwijaya
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Ine Elisa Putri
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Agus Arip Munawar
- Department of Agricultural Engineering, Faculty of Agriculture, Universitas Syiah Kuala, Indonesia
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Bwambok DK, Siraj N, Macchi S, Larm NE, Baker GA, Pérez RL, Ayala CE, Walgama C, Pollard D, Rodriguez JD, Banerjee S, Elzey B, Warner IM, Fakayode SO. QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6982. [PMID: 33297345 PMCID: PMC7730680 DOI: 10.3390/s20236982] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 12/23/2022]
Abstract
Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.
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Affiliation(s)
- David K. Bwambok
- Chemistry and Biochemistry, California State University San Marcos, 333 S. Twin Oaks Valley Rd, San Marcos, CA 92096, USA;
| | - Noureen Siraj
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Samantha Macchi
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Nathaniel E. Larm
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Gary A. Baker
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Rocío L. Pérez
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Caitlan E. Ayala
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Charuksha Walgama
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - David Pollard
- Department of Chemistry, Winston-Salem State University, 601 S. Martin Luther King Jr Dr, Winston-Salem, NC 27013, USA;
| | - Jason D. Rodriguez
- Division of Complex Drug Analysis, Center for Drug Evaluation and Research, US Food and Drug Administration, 645 S. Newstead Ave., St. Louis, MO 63110, USA;
| | - Souvik Banerjee
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - Brianda Elzey
- Science, Engineering, and Technology Department, Howard Community College, 10901 Little Patuxent Pkwy, Columbia, MD 21044, USA;
| | - Isiah M. Warner
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Sayo O. Fakayode
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
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Singpoonga N, Rittiron R, Seang-on B, Chaiprasart P, Bantadjan Y. Determination of Adenosine and Cordycepin Concentrations in Cordyceps militaris Fruiting Bodies Using Near-Infrared Spectroscopy. ACS OMEGA 2020; 5:27235-27244. [PMID: 33134685 PMCID: PMC7594118 DOI: 10.1021/acsomega.0c03403] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 10/02/2020] [Indexed: 06/11/2023]
Abstract
Near-infrared (NIRS) spectroscopy, coupled with partial least squares regression, was used to predict adenosine and cordycepin concentrations in fruiting bodies of Cordyceps militaris. The fruiting body samples were prepared in four different sample formats, which were intact fruiting bodies, chopped fruiting bodies, dried powder, and dried crude extract. The actual amount of the adenosine and cordycepin concentrations in fresh fruiting bodies was analyzed by high-performance liquid chromatography. Results showed that the prediction models developed from the chopped samples provided excellent accuracy in both parameters with minimal sample preparation. These optimum models provided a coefficient of determination of prediction, standard error of prediction, bias, and residual predictive deviation, which were respectively 0.95, 16.60 mg kg-1, -8.57 mg kg-1, and 5.04 for adenosine prediction, and 0.98, 181.56 mg kg-1, -1.05 mg kg-1, and 8.9 for cordycepin prediction. The accuracy and performance of the model were determined by ISO12099:2017(E). It was found that these two equations can be considered to be acceptable at a probability level of 95% confidence. The NIRS technique, therefore, has the potential to be an objective method for determining the adenosine and cordycepin concentrations in C. militaris fruiting bodies.
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Affiliation(s)
- Natthapong Singpoonga
- Department
of Biology and Biotechnology, Faculty of Science and Technology, Nakhon Sawan Rajabhat University, Nakhon Sawan 60000, Thailand
| | - Ronnarit Rittiron
- Department
of Food Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Nakhon Pathom 73140, Thailand
| | - Boonsong Seang-on
- Faculty
of Agriculture, Natural Resources and Environment, Naresuan University, Phitsanulok 65000, Thailand
- Center
of Excellence in Postharvest Technology, Naresuan University, Phitsanulok 65000, Thailand
| | - Peerasak Chaiprasart
- Faculty
of Agriculture, Natural Resources and Environment, Naresuan University, Phitsanulok 65000, Thailand
- Center
of Excellence in Postharvest Technology, Naresuan University, Phitsanulok 65000, Thailand
- Postharvest
Technology Innovation Center, Chiang Mai
University, Chiang Mai 50200, Thailand
| | - Yuranan Bantadjan
- Department
of Food Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Nakhon Pathom 73140, Thailand
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