Supervised Multivariate Analysis of Hyper-spectral NIR Images to Evaluate the Starch Index of Apples

By Paolo Menesatti, A. Zanella, S. D’Andrea, Corrado Costa, G. Paglia, Federico Pallottino. 2009

141 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biochemistry, Food Science

View Paper: https://www.semanticscholar.org/paper/572015b32f26babdf3b0dad4db15b13611cf77e4

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Citations in the corpus

Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry (2014, background): “2013a) and quality attributes (Menesatti et al. 2009; Liu et al. 2011; Barbin et al. 2012a) of food products.”
Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review (2013, methodology): “(78) applied the hyperspectral technique to determine the starch index of apples as a maturity parameter for predicting the optimal harvest period.”
Application of NIR hyperspectral imaging for discrimination of lamb muscles (2011, background): “The combined nature of imaging and spectroscopy in a hyperspectral imaging enabled this system to simultaneously provide physical and chemical characteristics of an object as well as their spatial distributions (ElMasry et al., 2008; Qiao et al., 2007a; Menesatti et al., 2009).”
Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment (2012, methodology): “…et al. (2004) Tomatoes PLS Constitutive compounds at different ripening stages Nicolaï et al. (2006) Apples PLS Identification of bitter pit lesions Menesatti et al. (2009) Golden Delicious apples PLS Assess starch index Taghizadeh et al. (2011b) White mushrooms PLS enzymatic browning Polder et…”
Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef (2019, methodology): “The PLSR is a robust and reliable chemometric tool for constructingmodels in spectral analysis (Menesatti et al. 2009).”
Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats (2015, unknown): “There are many effective algorithms for solving problems in classification, including linear classification algorithms such as soft independent modeling of class analogy (SIMCA) (Menesatti et al., 2009) and PLS-DA (Daszykowski, Orzel, Wrobel, CzarnikMatusewicz, & Walczak, 2011), and non-linear class”
Computer Vision System Applied to Classification of “Manila” Mangoes During Ripening Process (2014, methodology): “Menesatti et al. (2009) studied the starch index of apples using a hyperspectral imaging system in the range between 1,000 and 1,700 nm.”
Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review (2012, methodology): “( 2009 ) successfully applied a NIR hyperspectral imaging to evaluate starch index in apple, which is very crucial for maturity and harvest time determination.”
Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review (2012, methodology): “Consequently, efficient data processing methods are needed to reveal the truth hidden behind these hypercubes (Lorente et al., 2011; Menesatti et al., 2009).”
Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review (2014, methodology): “Applications by applying statistical texture especially co-occurrence matrix method can be found in ripeness evaluation of strawberries and apples (ElMasry, Wang, ElSayed, & Ngadi, 2007; Letal, Jirak, Suderlova, & Hajek, 2003), starch content assessment of apples (Menesatti et al., 2009), defect ins”