Recipient Organization
UNIV OF IDAHO
875 PERIMETER DRIVE
MOSCOW,ID 83844-9803
Performing Department
(N/A)
Non Technical Summary
Potatoes need to heal their wounds quickly to avoid water loss, diseases, and defects. This study is checking if we can make this happen by healing the wounds at lower curing temperatures. If it works, it can keep the potatoes fresh for longer, which could reduce losses during long-term storage. In the short term, we'll try to heal the wounds in russet potatoes by using calcium chloride and nitric oxide (NO) treatments at lower curing temperatures. We hope this will make the healing process more effective. In the medium term, we'll use a technique called Near-Infrared Hyperspectral Imaging (NIR-HSI) to see how much the wounds have healed. We'll take images of russet potatoes and analyze them to see how much they've healed. We'll use this to create models to predict the healing process. In the long term, we'll use this approach to see if it works in practical settings. We'll evaluate its impact on potato quality, shelf-life, and losses during long-term storage. If it works, it could reduce water loss and decay during prolonged storage. Also, we'll use the NIR-HSI technique to improve storage management practices. It could help us control the environment better and keep the potatoes fresh for longer. Ultimately, these achievements enhance profitability by upholding potato quality, extending shelf life, and curbing losses.
Animal Health Component
30%
Research Effort Categories
Basic
70%
Applied
30%
Developmental
(N/A)
Goals / Objectives
The overall hypothesis of the project is if the wound healing process can be enhanced at low curing temperatures, then potato shelf-life can be extended, and losses minimized during long-term storage. Therefore, the short-term goal of the project is to enhance the wound healing process of Russet potatoes using calcium and nitric oxide at low curing temperatures. The median-term goal is to acquire HSI of Russet potatoes (x, y) and the lignin /suberin content of these samples (λ) and develop prediction models using different chemometric approaches. Finally, the long-term goal is to apply this approach in real-life situations and verify the effect on quality, shelf-life, and potato losses during long-term storage.The specific objectives of this project are:Objective I: (Research) to develop technologies to enhance the wound healing process under low curing temperatures.Objective II: (Research) to develop a non-destructive model to determine the wound healing process by means of HSI and suberin/lignin content in the periderm.By accomplishing these specific objectives, we expect that the rapid wound healing process at low curing temperatures will reduce weight loss and decay in potatoes during long-term storage. We also expect that a non-destructive wound healing measurement using HSI will contribute to the potato industry by improving (on-time / in situ) control processes of the storage environment. All in all, this can increase profitability by maintaining potato quality, extending shelf-life, and reducing losses.
Project Methods
Plant material'Clearwater Russet' potatoes will be used as it is a dual-purpose potato cultivar with good cold-induced sweetening resistance and exhibits excellent fry color out of long-term storage. This cultivar was accepted by McDonald's in 2016 and the acreage has increased tenfold since then.Obj. I: Development of technologies to improve the wound healing process under low curing temperaturesThe general objective of this study is to understand the effect of different products on the biochemistry and physiology of the wound-healing process of potatoes. Thus, an efficient and economical method can be developed to enhance wound healing at low curing temperature conditions and to maintain potato quality during storage.Wound healing experiment: Tubers will be treated with i. control (without any treatment), ii. nitric oxide gaseous treatment (1 mMol/L) for 5 hours in a sealed cabinet with an internal ventilation fan, as suggested by Zhu et al. (2009), and iii. low-volume spray application of 0.2 M solution of food-grade calcium chloride (CaCL2) at a rate of 0.5 gal/ton (Miller et al., 2011). After that, the wound healing process will take place at 10°C / 50°F and 13°C / 55°F with 95% relative humidity (RH) for 14 days. The experiments will be set according to a complete randomized design (CRD) in a factorial arrangement 3 (treatments) x 2 (curing temperatures) x 8 (withdraws, 0, 2, 4, 6, 8, 10, 12, and 14 days) x 3 repetitions of 10 potatoes.Storage experiment: Following the wound healing process, the temperatures will be ramped down to 7.2°C / 45°F, 0.5°F per day. Tubers will be stored at 7.2°C / 45°F and 95% RH for up to 8 months. The experiments will be set according to a complete randomized design (CRD) in a factorial arrangement 3 (treatments) x 2 (curing temperatures) x 5 (withdraws, 0, 2, 4, 6, 8 months) x 3 repetitions of 10 potatoes.Evaluations:a) Wound healing experiment: Fresh weight loss will be determined during the wound healing process every 2 days and on the individual potato tuber cores, this would provide an indirect indication of the impact of wound healing. Wound healing will be evaluated according to the suberin deposition as described by Rui et al. (2021). Periderm samples will be taken every 2 days, immediately frozen using liquid nitrogen, and stored at -80°C / -112°F. After that, periderm samples will be freeze-dried, and ground for the phenylalanine ammonia-lyase (PAL) activity determination following the method described by Flores et al. (2014). Phenolic compounds content will be measured using High-Performance Liquid Chromatography (HPLC) according to Whitehead and Poveda (2019). Fresh tubers will be used for the respiration rate determination (mg CO2 kg-1 h-1) using a static method as described by Saltveit (2003). The RNA-Seq will be conducted at the University of Florida ICBR RNA-sequencing facilities to evaluate the differentially expressed genes (Dr. Liu - collaboration).b) Storage experiment: During the storage period, potatoes will be evaluated every two months for weight loss, specific gravity, sugar content, fry color, and sprouting (Wang et a., 2016).Statistical evaluation: The data will be submitted for analysis of variance and the means will be compared using Tukey's test at a 0.05% probability level using the software R (R Core Team, 2020, Auckland, New Zealand.Obj. II: Development of non-destructive models to determine the wound healing process by means of hyperspectral imaging and suberin/lignin content in the skinThe general objective of this study is to develop a non-destructive model to determine the wound healing process by means of HSI and suberin/lignin content in the periderm. Therefore, a non-destructive wound healing measurement using HSI will contribute to the potato industry by improving (on-time / in situ) control processes of the storage environment.Wound healing experiments (2024-2025 and 2025-2026): Tubers samples from the two-year experiments will be used for the NIR-HIS acquisition. Thus, images of 10 tubers from the previously described treatments (control - without any treatment, 1 mMol/L NO for 5 hours, and 0.2 M CaCL2) will be obtained at days 0, 2, 4, 6, 8, 10, 12, and 14 after the treatment applications.HSI acquisition: The images will be acquired with a Specim FX17 camera (Specim, Spectral Imaging Ltd, Oulu, Finland). The camera comprises an imaging spectrograph coupled to an indium gallium arsenide (InGaAs) detector. Individual images will be collected within a spectral range of 900 to 1700 nm at 8 nm, with spectral sampling per pixel of 3.5 nm. Images of the entire potatoes will be collected at 0, 2, 4, 6, 8, 10, 12, and 14 days after the wound healing treatment applications. Four images will be taken at 8-time intervals. White and dark references will be captured prior to each sample image and subsequently used for image correction and calibration.HSI analysis: The Unscrambler version 10.3 (Camo, Oslo, Norway) will be used for HSI handling and processing. Depending on the case, the hyperspectral data will be processed using Evince v.2.32.0 (UmBio AB, Umeå, Sweden) and MATLAB® 8.9 (The MathWorks Inc., Natick, MA, USA) ambient with PLS Toolbox 8.9 (Eigenvector Research, Inc, Manson, WA, USA) platform and algorithms developed in the laboratory. The image calibration and correction to absorbance will be conducted using Evince package as proposed by Williams et al. (2012). Each individual image will be merged to constitute a mosaic of images. Thus, each mosaic will include the control and the images taken at different time periods for the corresponding treatments.PCA: The regions of interest will be located and identified on the potato skin by applying the PCA score plots, score images, and loading line plots. However, pre-processing techniques such as brushing (Manley et al., 2011) will be previously used to remove irrelevant pixels and recalculate the PCA.PLS: The NIR-HSI of potato skin at different moments of the wound healing process (X matrix) and the suberin/lignin contents (Y matrix) data will be used for the development of the classification and prediction models. As the NIR-HSI of fresh vegetables are highly convoluted and are affected by scattering effects, tissue heterogeneities, instrumental noise, ambient effects, and other sources of variability8, the spectra will be submitted to different pre-processing procedures: standard normal variate, (SNV) and multiplicative scatter correction (MSC) aiming to reduce the influence of light scattering57. Pre-processing can be used isolated or in combination depending on the modeling performance results. The HSI (n=1,920) will be divided into calibration/training (n=1,278) and validation sets (n=641) by applying the classic Kennard-Stone (KS) selection algorithm59. Outliers will also be detected and executed to improve the model's accuracy by removing samples with extreme values, which exhibit increased influence on the model and eliminate unmodelled residues in the X and Y data responses. An elliptical joint confidence region (EJCR) will be calculated to evaluate the slope and intercept for the reference regression and predict values at a 95% confidence interval60,61. PLS with leave-one-out cross-validation will be used and the optimal number of latent variables (LV) will be determined by minimizing the predicted residual error sum of squares (PRESS). The performance of the PLS calibration models will be evaluated using the coefficient of correlation (R2), RMSEC, and cross-validation (RMSECV). With the validation set the RMSEP (SEP) will be obtained8. The images of each potato will be divided into quadrants (rectangles consisting of approximately the same number of pixels), averaged, pre-processed, and the PLS model will be used to obtain the highest number of objects using leave-one-out cross-validation (Williams et al., 2012).