Development of a near-infrared spectroscopy method (NIRS) for fast analysis of total, indolic, aliphatic and individual glucosinolates in new bred open pollinating genotypes of broccoli ( Brassica oleracea convar. botrytis var. italica )

Fritz

2020

Plant Methods

Background Glucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method. Results Here we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R2 = 0.75 with RPD = 2.3 for predicting µmol GBS·100 g−1 fresh weight and R2 = 0.80 with RPD = 2.4 for predicting µmol GBS·g−1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O–H stretching and/or N–H stretching of amines. Conclusions A calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.

Section: Discussionmentioning

confidence: 52%

“…Only four of these studies reported calibration models for GBS specifically ( Table 1). Of those, Sahamishirazi et al [27] reported a model that had poor performance in broccoli florets. In B. rapa leaf tissue, Font et al [28] were able to determine low or high GBS concentrations for screening purposes.…”

mentioning

confidence: 99%

Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue

Fritz

2020

Plant Methods

“…The models reported here outperform several others [27][28][29]. The model developed by Chen et al [30] performed better than the present study, and it was useful for qualitative determination of GBS in Chinese kale.…”

Section: Discussionsupporting

confidence: 38%

“…Six studies have attempted to develop quantitative calibration models for leaf tissue of several GSL-containing plants with various degrees of success [25][26][27][28][29][30], and four of these included GBS for calibration based on dry weight reference values (Table 1). Of those, Sahamishirazi et al [27] reported a model that had poor performance in broccoli orets. In B. rapa leaf tissue, Font et al [28] were able to determine low or high GBS concentrations for screening purposes.…”

Section: Introductionmentioning

confidence: 99%

Using Near-Infrared Reflectance Spectroscopy (NIRS) to Predict Glucobrassicin Concentrations in Cabbage and Brussels Sprout Leaf Tissue

Fritz²

2020

Preprint

BackgroundGlucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method. ResultsHere we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R2=0.75 with RPD=2.3 for predicting µmol GBS·100g-1 fresh weight and R2=0.80 with RPD=2.4 for predicting µmol GBS·g-1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O-H stretching and/or N-H stretching of amines. ConclusionsA calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.

“…Only four of these studies reported calibration models for GBS speci cally (table 1). Of those, Sahamishirazi et al [27] reported a model that had poor performance in broccoli orets. In B. rapa leaf tissue, Font et al [28] were able to determine low or high GBS concentrations for screening purposes.…”

Section: Introductionmentioning

confidence: 99%

Using Near-infrared Reflectance Spectroscopy (NIRS) to Predict Glucobrassicin Concentrations in Cabbage and Brussels Sprout Leaf Tissue

Fritz²

2020

Preprint

BackgroundGlucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method. ResultsHere we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R2=0.75 with RPD=2.3 for predicting µmol GBS·100g-1 fresh weight and R2=0.80 with RPD=2.4 for predicting µmol GBS·g-1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O-H stretching and/or N-H stretching of amines. ConclusionsA calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.