NIR spectroscopy for rapid measurement of moisture and cannabinoid contents of industrial hemp (Cannabis sativa)

Kai, Shoichi; Maghirang, Elizabeth B.; Tan, Jia W.; Yoon, Ju Young; Armstrong, Paul R.; Kachroo, Pradeep; Hildebrand, David

doi:10.1016/j.indcrop.2022.115007

Cited by 14 publications

(17 citation statements)

References 18 publications

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“…These spectral regions were identified using iPLS with an interval size of five variables on the whole calibration data set using a 90/10 cross-validation. For a dedicated band assignment of pure cannabis, the reader is referred to [14]. As clearly visible in the averaged preprocessed spectra, the region between 1680 nm and 1735 nm is dominated by the influences of the chosen plastic bag and is therefore excluded by the iPLS and thus not considered in the calibrated PLS-DA model.…”

Section: Pls-da Model Calibrationmentioning

confidence: 99%

“…However, these methods do not provide the necessary measurement precision to reliably distinguish between legal and illegal samples. Nevertheless, optical measurement techniques, which allow measurement of the THC concentration in cannabis, including e.g., near-infrared (NIR) [12][13][14], mid-infrared (MIR) [15], and Raman spectroscopy [16,17], have been presented in the literature. Mostly, published works on THC measurements and commercially available optical cannabis sensors [18][19][20] focus on potency measurements, i.e., measurement of total THC concentration in the investigated samples.…”

Section: Introductionmentioning

confidence: 99%

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Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy

Zimmerleiter,

Greibl,

Meininger

et al. 2024

Sensors

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A rugged handheld sensor for rapid in-field classification of cannabis samples based on their THC content using ultra-compact near-infrared spectrometer technology is presented. The device is designed for use by the Austrian authorities to discriminate between legal and illegal cannabis samples directly at the place of intervention. Hence, the sensor allows direct measurement through commonly encountered transparent plastic packaging made from polypropylene or polyethylene without any sample preparation. The measurement time is below 20 s. Measured spectral data are evaluated using partial least squares discriminant analysis directly on the device’s hardware, eliminating the need for internet connectivity for cloud computing. The classification result is visually indicated directly on the sensor via a colored LED. Validation of the sensor is performed on an independent data set acquired by non-expert users after a short introduction. Despite the challenging setting, the achieved classification accuracy is higher than 80%. Therefore, the handheld sensor has the potential to reduce the number of unnecessarily confiscated legal cannabis samples, which would lead to significant monetary savings for the authorities.

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Section: Pls-da Model Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy

Zimmerleiter,

Greibl,

Meininger

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Nevertheless, although these analytical methods possess good interpretability and are reliable, they still have quite a lot of disadvantages, including expensive costs and complex operations, and are timeconsuming, which have limited their promotion and application. 11 Therefore, there is an imperative need for proposing a rapid and accurate method to control the quality of Atractylodis rhizoma based on the overall properties. Spectroscopic technologies have become more and more popular in recent years attributed to their fast detection speed.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, hinesol, atractylon, and β-eudesmol were identified as potential Q-markers for Atractylodis rhizoma . Nevertheless, although these analytical methods possess good interpretability and are reliable, they still have quite a lot of disadvantages, including expensive costs and complex operations, and are time-consuming, which have limited their promotion and application . Therefore, there is an imperative need for proposing a rapid and accurate method to control the quality of Atractylodis rhizoma based on the overall properties.…”

Section: Introductionmentioning

confidence: 99%

Fast Discrimination and Quantification Analysis of Atractylodis rhizoma Using NIR Spectroscopy Coupled with Chemometrics Tools

Peng,

He,

Wang

et al. 2024

J. Agric. Food Chem.

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In this study, near-infrared (NIR) spectroscopy and high-performance liquid chromatography (HPLC) combined with chemometrics tools were applied for quick discrimination and quantitative analysis of different varieties and origins of Atractylodis rhizoma samples. Based on NIR data, orthogonal partial least squares discriminant analysis (OPLS-DA) and K-nearest neighbor (KNN) models achieved greater than 90% discriminant accuracy of the three species and two origins of Atractylodis rhizoma. Moreover, the contents of three active ingredients (atractyloxin, atractylone, and β-eudesmol) in Atractylodis rhizoma were simultaneously determined by HPLC. There are significant differences in the content of the three components in the samples of Atractylodis rhizoma from different varieties and origins. Then, partial least squares regression (PLSR) models for the prediction of atractyloxin, atractylone, and β-eudesmol content were successfully established. The complete Atractylodis rhizoma spectra gave rise to good predictions of atractyloxin, atractylone, and β-eudesmol content with R 2 values of 0.9642, 0.9588, and 0.9812, respectively. Based on the results of this present research, it can be concluded that NIR is a great nondestructive alternative to be applied as a rapid classification system by the drug industry.

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“…Industrial hemp (Hemp sativa L.), which has less than 0.3% ∆9-tetrahydrohempnol (THC), is a non-toxic and multipurpose cash crop [1]. In the past decade, with the development and utilization of industrial hemp in agriculture, medicine, construction, and the paper industry [2,3], the economic value of industrial hemp has been paid increasing attention [4]. Oil seeds and cannabidiol (CBD) are considered the largest and most promising markets for hemp production in North America and Europe [2,5].…”

Section: Introductionmentioning

confidence: 99%

Changes in Rhizosphere Soil Nutrients, Enzyme Activities, and Microbial Communities at Different Stages of Industrial Hemp Development

Guo

Ma²,

Wang

et al. 2022

Agronomy

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Determining the nutrient requirements of industrial hemp to increase the yield requires quantifying variations in soil nutrients and enzyme activities in different growth stages, along with relevant soil microbial response. This study investigated the effects of different growth stages of industrial hemp on rhizosphere soil nutrients, enzyme activities, and microbial communities. The results showed that with the increase in the growth stages, the pH and available phosphorus (AP) decreased, while the soil organic matter (SOM), available nitrogen (AN), and available potassium (AK) increased substantially, indicating that the demand for nutrients of industrial hemp was constantly changing. Proteobacteria, Acidobacteria, Ascomycota, and Basidiomycota were found to be the keystone taxa to adapt to the nutrient requirements of industrial hemp at different growth stages by regulating soil enzyme activity. Furthermore, using the redundancy analysis and Spearman’s correlation analysis, we found that microbial taxonomic composition was related to the variations in AN, AP, and pH. In general, we emphasized that the interaction between industrial hemp and soil is closely related to the growth stage, which increases plant adaptability and growth because of the change of soil microorganisms.

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NIR spectroscopy for rapid measurement of moisture and cannabinoid contents of industrial hemp (Cannabis sativa)

Cited by 14 publications

References 18 publications

Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy

Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy

Fast Discrimination and Quantification Analysis of Atractylodis rhizoma Using NIR Spectroscopy Coupled with Chemometrics Tools

Changes in Rhizosphere Soil Nutrients, Enzyme Activities, and Microbial Communities at Different Stages of Industrial Hemp Development

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