Domain adaptation for regression under Beer–Lambert’s law

Nikzad‐Langerodi, Ramin; Zellinger, Werner; Saminger‐Platz, Susanne; Moser, Bernhard

doi:10.1016/j.knosys.2020.106447

Cited by 52 publications

(29 citation statements)

References 23 publications

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“…The value of NDSWI is directly proportional to the water content in the strawberry fruit. The concentration of water can be related to the absorbance of electromagnetic radiation as per Beer-Lambert law [31,42]. We use a modified Beer-Lambert law to create the Strawberry Fruit Water Content (SFWC) metric:…”

Section: Strawberry Fruit Water Content Metricmentioning

confidence: 99%

Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing

2022

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We propose data-driven approaches to water content estimation and ripeness classification of the strawberry fruit. A narrowband hyperspectral spectroradiometer was used to collect reflectance signatures from 43 strawberry fruits at different ripeness levels. Then, the ground truth water content was obtained using the oven-dry method. To estimate the water content, 674 and 698 nm bands were selected to create a normalized difference strawberry water content index. The index was used as an input to a logarithmic model for estimating fruit water content. The model for water content estimation gave a correlation coefficient of 0.82 and Root Mean Squared Error (RMSE) of 0.0092 g/g. For ripeness classification, a Support Vector Machine (SVM) model using the full spectrum as input achieved over 98% accuracy. Our analysis further show that, in the absence of the full spectrum data, using our proposed water content index as input, which uses reflectance values from only two frequency bands, achieved 71% ripeness classification accuracy, which might be adequate for certain applications with limited sensing resources.

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Section: Strawberry Fruit Water Content Metricmentioning

confidence: 99%

Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing

2022

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“…This, however, is prohibitive for small samples and/or if the relationship between inputs (e.g., spectra) and the response (e.g., concentration) is approximately linear. 49,50 …”

Section: Previous Workmentioning

confidence: 99%

“…It is important to acknowledge that, in general, the component‐wise covariance difference matrix is not positive semi‐definite and the optimization problem in Equation () thus not convex if

boldG = C_{S} - C_{T}

. However, the symmetry of the covariance difference matrix, which implies orthonormal eigenvectors

boldU = false[{boldu}_{1}, \dots, {boldu}_{p} false]

, can be exploited to derive a convex restriction of the objective function by letting

(||, {bold-italicC}_{S} - {bold-italicC}_{T}) = boldU diag false(false| λ_{1} false|, \dots, false| λ_{p} false| false) {boldU}^{normalT}

with | λ i | being the absolute value of the i‐ th eigenvalue of the covariance difference matrix 50 …”

Section: Previous Workmentioning

confidence: 99%

A chemometrician's guide to transfer learning

Nikzad‐Langerodi

Andries

2021

Journal of Chemometrics

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Transfer learning (TL), the sub‐discipline of machine learning devoted to learning from different domains, has gained increasing attention over the past decade. With the current contribution, we aim at giving a concise overview on theory, concepts, and applications of TL from a chemometrician's perspective and draw some connections to previous work on calibration model updating/adaptation and calibration transfer. Furthermore, we provide a demonstration of the application of TL in analytical chemistry and discuss the benefits and challenges associate with its use for real‐world problems. We conclude the paper by discussing some open problems and by contemplating on future research directions.

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“…We refer the reader to Marco and Gutierrez-Galvez (2012) or Rudnitskaya (2018) for a more exhaustive review of existing methods, which include for instance transfer learning approaches (Nikzad-Langerodi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%