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	<title>Outliers Detection &#8211; NIRPY Research</title>
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		<title>Robust MSC</title>
		<link>https://nirpyresearch.com/robust-msc/</link>
		
		<dc:creator><![CDATA[Daniel Pelliccia]]></dc:creator>
		<pubDate>Sun, 05 Oct 2025 04:07:20 +0000</pubDate>
				<category><![CDATA[Regression]]></category>
		<category><![CDATA[Data Correction and Normalisation]]></category>
		<category><![CDATA[Outliers Detection]]></category>
		<category><![CDATA[Partial Least Squares Regression]]></category>
		<category><![CDATA[Regression pipelines]]></category>
		<guid isPermaLink="false">https://nirpyresearch.com/?p=3384</guid>

					<description><![CDATA[<img width="666" height="499" src="https://nirpyresearch.com/wp-content/uploads/2025/02/robust_MSC.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin-bottom: 10px; clear: both; max-width: 100%;" decoding="async" fetchpriority="high" />Robust MSC is an implementation of multiplicative scatter correction that is better suited at handling outliers in the original spectra.]]></description>
		
		
		
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		<title>Robust PCA</title>
		<link>https://nirpyresearch.com/robust-pca/</link>
		
		<dc:creator><![CDATA[Daniel Pelliccia]]></dc:creator>
		<pubDate>Sun, 17 Mar 2024 08:43:04 +0000</pubDate>
				<category><![CDATA[Data Operations and Plotting]]></category>
		<category><![CDATA[Data Correction and Normalisation]]></category>
		<category><![CDATA[Outliers Detection]]></category>
		<category><![CDATA[Principal Components Analysis]]></category>
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					<description><![CDATA[<img width="666" height="499" src="https://nirpyresearch.com/wp-content/uploads/2024/03/RobustPCA.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="Robust PCA image post" style="display: block; margin-bottom: 10px; clear: both; max-width: 100%;" decoding="async" />A implementation of robust PCA, useful when the data contains outliers.]]></description>
		
		
		
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		<title>Detecting outliers using the Mahalanobis distance with PCA in Python</title>
		<link>https://nirpyresearch.com/detecting-outliers-using-mahalanobis-distance-pca-python/</link>
		
		<dc:creator><![CDATA[Daniel Pelliccia]]></dc:creator>
		<pubDate>Sun, 20 Jan 2019 05:13:20 +0000</pubDate>
				<category><![CDATA[Classification]]></category>
		<category><![CDATA[Data Operations and Plotting]]></category>
		<category><![CDATA[Outliers Detection]]></category>
		<category><![CDATA[Principal Components Analysis]]></category>
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					<description><![CDATA[<img width="680" height="272" src="https://nirpyresearch.com/wp-content/uploads/2019/01/mahalanobis-distance-pca-1024x410.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="mahalanobis-distance-pca" style="display: block; margin-bottom: 10px; clear: both; max-width: 100%;" decoding="async" />How do we make sure we are detecting only true outliers and not cherry-picking from the data? Here's a method based on the Mahalanobis distance with PCA.]]></description>
		
		
		
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		<title>Outliers detection with PLS regression for NIR spectroscopy in Python</title>
		<link>https://nirpyresearch.com/outliers-detection-pls-regression-nir-spectroscopy-python/</link>
		
		<dc:creator><![CDATA[Daniel Pelliccia]]></dc:creator>
		<pubDate>Sat, 22 Sep 2018 06:57:37 +0000</pubDate>
				<category><![CDATA[Data Operations and Plotting]]></category>
		<category><![CDATA[Outliers Detection]]></category>
		<category><![CDATA[Partial Least Squares Regression]]></category>
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					<description><![CDATA[<img width="680" height="273" src="https://nirpyresearch.com/wp-content/uploads/2018/09/outliers-detection-1024x411.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="Outliers detection with PLS regression for NIR spectroscopy in Python" style="display: block; margin-bottom: 10px; clear: both; max-width: 100%;" decoding="async" loading="lazy" />Not every data point is created equal. In this post we'll show how to perform outliers detection with PLS regression for NIR spectroscopy in Python.]]></description>
		
		
		
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