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		<title>On the calculation of the entropy of a signal</title>
		<link>https://nirpyresearch.com/calculation-entropy-signal/</link>
		
		<dc:creator><![CDATA[Daniel Pelliccia]]></dc:creator>
		<pubDate>Fri, 03 Apr 2026 05:53:30 +0000</pubDate>
				<category><![CDATA[Data Correction and Normalisation]]></category>
		<category><![CDATA[Data Operations and Plotting]]></category>
		<category><![CDATA[Smoothing]]></category>
		<category><![CDATA[entropy]]></category>
		<category><![CDATA[first derivative]]></category>
		<category><![CDATA[noise]]></category>
		<category><![CDATA[optimisation]]></category>
		<category><![CDATA[savgol filter]]></category>
		<category><![CDATA[signal]]></category>
		<category><![CDATA[smoothing]]></category>
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					<description><![CDATA[<img width="666" height="499" src="https://nirpyresearch.com/wp-content/uploads/2026/04/pdia-f3e715a2-9a4a-49d6-bec2-c5a930d1985b.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin-bottom: 10px; clear: both; max-width: 100%;" decoding="async" fetchpriority="high" />The calculation of the information entropy requires an arbitrary step in the computation of the histogram of a signal. Here's some more details on this technical step.]]></description>
		
		
		
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		<title>Spectra smoothing based on information entropy</title>
		<link>https://nirpyresearch.com/spectra-smoothing-based-on-information-entropy/</link>
		
		<dc:creator><![CDATA[Daniel Pelliccia]]></dc:creator>
		<pubDate>Sat, 17 Jan 2026 07:34:28 +0000</pubDate>
				<category><![CDATA[Data Correction and Normalisation]]></category>
		<category><![CDATA[Data Operations and Plotting]]></category>
		<category><![CDATA[Smoothing]]></category>
		<category><![CDATA[entropy]]></category>
		<category><![CDATA[first derivative]]></category>
		<category><![CDATA[noise]]></category>
		<category><![CDATA[optimisation]]></category>
		<category><![CDATA[savgol filter]]></category>
		<category><![CDATA[signal]]></category>
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					<description><![CDATA[<img width="666" height="499" src="https://nirpyresearch.com/wp-content/uploads/2026/01/pdia-7b5a52c3-9e9a-4e4c-a547-f1b0366b9c91.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin-bottom: 10px; clear: both; max-width: 100%;" decoding="async" />The concept of information entropy can be used to optimise the parameters of your smoothing function. Here's an idea on how to do that. ]]></description>
		
		
		
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		<title>Principal Component Regression in Python</title>
		<link>https://nirpyresearch.com/principal-component-regression-python/</link>
		
		<dc:creator><![CDATA[Daniel Pelliccia]]></dc:creator>
		<pubDate>Fri, 11 May 2018 21:11:15 +0000</pubDate>
				<category><![CDATA[Principal Components Regression]]></category>
		<category><![CDATA[Regression]]></category>
		<category><![CDATA[calibration]]></category>
		<category><![CDATA[cross-validation]]></category>
		<category><![CDATA[dimensionality reduction]]></category>
		<category><![CDATA[first derivative]]></category>
		<category><![CDATA[grid-search]]></category>
		<category><![CDATA[mse]]></category>
		<category><![CDATA[optimisation]]></category>
		<category><![CDATA[overfitting]]></category>
		<category><![CDATA[pca]]></category>
		<category><![CDATA[pipeline]]></category>
		<category><![CDATA[r2]]></category>
		<category><![CDATA[rmse]]></category>
		<category><![CDATA[savgol filter]]></category>
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					<description><![CDATA[<img width="640" height="480" src="https://nirpyresearch.com/wp-content/uploads/2018/05/principal-component-regression-python-mseplot.png" class="webfeedsFeaturedVisual wp-post-image" alt="Principal Component Regression in Python" style="display: block; margin-bottom: 10px; clear: both; max-width: 100%;" decoding="async" />An in-depth introduction to Principal Component Regression in Python using NIR data. PCR is the combination of PCA with linear regression. Check it out.]]></description>
		
		
		
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