# Data smoothing in temperature measurements from MLX90614 BCC IR sensor

I am trying to measure temperature with the MLX90614 BCC IR sensor for my project, and I am having problems with my precision requirement. I had taken data for 1 second then took the mean of these values. I measured the hand of a human, which was 36.2°C. I took measurements for different distances also like 1 cm, 3 cm, and 5 cm. However, sometimes there is more than 0.1 °C difference between the mean of all measurements( I take measurements for 1 second always), and my goal is to compensate this difference to < 0.1 °C. I tried median, moving average, Savitzky-Golay filter, and a couple more, but none of them lowers the difference. When I checked the histogram of my data, I saw that even for a constant distance from the sensor, there is 0.4 °C variance. Is there any way to compensate for this difference by a filter, or do I need to use more physical requirements while taking measurements? Here are my histograms of data measurements for different distances and mean values: Mean of the first figure - unfiltered: 32.2072 Mean of the second figure - unfiltered: 32.3612 Mean of the third figure - unfiltered: 32.2749 Mean of the fourth - unfiltered: 32.2735 Mean of the fifth figure - unfiltered: 32.2243 Mean of the sixth figure - unfiltered: 32.1224 Mean of the seventh - unfiltered: 32.0925 Mean of the last figure - unfiltered: 32.2129 In summary, is there any way to decrease these mean differences up to max 0.1.