Hyperspectral imaging with chemometrics for non-destructive determination of cannabinoids in floral and leaf materials of industrial hemp (Cannabis sativa L.)

Lu, Yutong; Li, Xu; Young, Sierra; Li, Xin; Linder, Eric; Suchoff, David H.

doi:10.1016/j.compag.2022.107387

Cited by 10 publications

(5 citation statements)

References 41 publications

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“…Similar to PCA, PLS-R has the ability to reduce the high dimensionality of data matrices [67]. Practically, PLS-R has the ability to predict plant traits [76,77], food chemical contents [78], and other characteristics. In addition, various chemometric techniques, such as partial least squared discriminant analysis (PLS-DA), support vector machine regression (SVM-R), and least squared support vector machine (LS-SVM), can be applied.…”

Section: Chemometric Modelsmentioning

confidence: 99%

Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits

Hernanda,

Lee,

Lee

2023

Applied Sciences

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The efficiency of hyper- and multispectral imaging (HSI and MSI) has gained considerable attention in research on plant phenotyping. This is due to their ease of use while being considered a nondestructive technology. Unlike current point-scanned spectroscopy, both HSI and MSI extract spatial and spectral information while covering a wide range of a plant body. Moreover, it is necessary to equip the extracted information with multivariate calibration techniques, followed by model evaluation. To date, the application of HSI and MSI for monitoring plant growth under a controlled environment is emerging and showing a good trend. Our systematic literature review discusses spectroscopy imaging techniques and their chemometric approaches as a sustainable sensor technology to detect plant traits. In conclusion, we also explore the possibility of carrying out HSI and MSI during plant trait analysis.

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Section: Chemometric Modelsmentioning

confidence: 99%

Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits

Hernanda,

Lee,

Lee

2023

Applied Sciences

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“…Owing to PLS-R being one of the most employed multivariate techniques, numerous articles describe the use of this approach for developing calibration models for various matrices in NIRS. In the particular case of Cannabis, PLS-R is usually employed for the construction of regression models for cannabinoids [22,36,[42][43][44]55,69,70,87] and/or terpenes [20], among other parameters [36,88]. Birenboim et al [20] describe the use of Fourier transform near-infrared spectroscopy (FT-NIR) to determine cannabinoid and terpene content in Cannabis inflorescence samples.…”

Section: Partial Least Squares Regressionmentioning

confidence: 99%

“…The proposed model displayed significant potential as a screening technique for implementation in the cultivation of this crop. Additionally, Lu et al [44] describe a HSI technology for non-destructive quantification of major cannabinoids, including CBD, ∆ 9 -THC (tetrahydrocannabinol), CBG (cannabigerol) and their acid forms in fresh floral and leaf materials of industrial hemp on a dry weight basis. Parsimonious PLS models were utilized, obtaining the best RPD values of 2.6 for CBD and ∆ 9 -THC in flowers.…”

Section: Near-infrared Hyperspectral Imagingmentioning

confidence: 99%

“…Additionally, it is a low-cost and rapid technique that can provide results within a few minutes, which is especially useful for high-throughput analysis or applications where quick results are required [42]. Since it is a non-destructive technique, samples can be used after NIRS measurements for other applications [36,43], which is particularly advantageous when working with valuable or limited sample quantities [44]. On the other hand, its versatility allows broad application across various sample types, including solids, liquids, and gases, being particularly adept at simultaneous analysis of multiple components within a sample [45].…”

Section: Introductionmentioning

confidence: 99%

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Light and Shadow in Near-Infrared Spectroscopy: A Powerful Tool for Cannabis sativa L. Analysis

Díaz-Liñán,

Sánchez de Medina,

Ferreiro-Vera

et al. 2023

AppliedChem

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Cannabis sativa L. is an ancient cultivar that has found applications in various fields, e.g., medicine, due to its beneficial effects. However, due to its psychotropic effects, the regulation of this cultivar has increased throughout the decades. In this context, the need for rapid and reliable analytical methods to ensure the quality control of Cannabis cultivars has become of extreme importance. NIRS has arisen as a powerful tool in this field due to its multiple advantages, e.g., non-destructive, rapid, and cost-effective. In this article, the chemometric techniques commonly employed in NIRS method development are described, along with their application for the analysis of Cannabis samples. Regarding qualitative methods, different mathematical treatments and classification models are explained. As for quantitative methods, the representative linear and non-linear modelling techniques applied for the development of prediction equations are described, alongside their application in the Cannabis field. To the best of our knowledge, this is the first time this type of review is written, since there are several articles which address cannabinoid determination, but the main purpose of this review is to enhance the potential of NIRS over the traditional techniques employed for the analysis of Cannabis samples.

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“…Machine learning approaches attained high classification accuracies up to 100% for factors like plant component and growth stage. Additionally, this technology was combined with chemometrics for non-destructive measurement of key cannabinoids in industrial hemp flowers and leaves [17]. However, no published studies have leveraged spectral imaging specifically for early diagnosis of nutrient deficiencies in industrial hemp prior to symptom manifestation.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging

Sanaeifar,

Yang,

Min

et al. 2024

Remote Sensing

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Hyperspectral imaging is an emerging non-invasive technology with potential for early nutrient stress detection in plants prior to visible symptoms. This study evaluated hyperspectral imaging for early identification of nitrogen, phosphorus, and potassium (NPK) deficiencies across three greenhouse-grown industrial hemp plant cultivars (Cannabis sativa L.). Visible and near-infrared spectral data (380–1022 nm) were acquired from hemp samples subjected to controlled NPK stresses at multiple developmental timepoints using a benchtop hyperspectral camera. Robust principal component analysis was developed for effective screening of spectral outliers. Partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) were developed and optimized to classify nutrient deficiencies using key wavelengths selected by variable importance in projection (VIP) and interval partial least squares (iPLS). The 16-wavelength iPLS-C-SVM model achieved the highest precision of 0.75 to 1 on the test dataset. Key wavelengths for effective nutrient deficiency detection spanned the visible range, underscoring the hyperspectral imaging sensitivity to early changes in leaf pigment levels prior to any visible symptom development. The emergence of wavelengths related to chlorophyll, carotenoid, and anthocyanin absorption as optimal for classification, highlights the technology’s capacity to detect subtle impending biochemical perturbations linked to emerging deficiencies. Identifying stress at this pre-visual stage could provide hemp producers with timely corrective action to mitigate losses in crop quality and yields.

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Hyperspectral imaging with chemometrics for non-destructive determination of cannabinoids in floral and leaf materials of industrial hemp (Cannabis sativa L.)

Cited by 10 publications

References 41 publications

Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits

Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits

Light and Shadow in Near-Infrared Spectroscopy: A Powerful Tool for Cannabis sativa L. Analysis

Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging

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