Arduino: How to eliminate thermocouple amplier noise - filter

Question

Scenario:
I m experimenting the thermocouple amplifier (SN-6675) with Arduino DUE.
After i'm included MAX6675 library, Arduino can measured room temperature.
However, Temp measured from arduino have 2 issues,
1) offset compare to "Fluke thermometer"
2) have tons of noise, and keep fluctuated after taking average of each 5 temperature sample.
eg, Fluke thermometer got 28.9C at room temp, arduino got 19.75~45.75C
Question: Any method/filter to reduce the measured noise, and gives a steady output?
Codes is attached for reference.
#include <MAX6675.h>
//TCamp Int
int CS = 7; // CS pin on MAX6675
int SO = 8; // SO pin of MAX6675
int SCKpin = 6; // SCK pin of MAX6675
int units = 1; // Units to readout temp (0 = ˚F, 1 = ˚C)
float error = 0.0; // Temperature compensation error
float tmp = 0.0; // Temperature output variable
//checking
int no = 0;
MAX6675 temp0(CS,SO,SCKpin,units,error); // Initialize the MAX6675 Library for our chip
void setup() {
Serial.begin(9600); // initialize serial communications at 9600 bps:
}
void loop() {
no= no + 1;
tmp = temp0.read_temp(5); // Read the temp 5 times and return the average value to the var
Serial.print(tmp);
Serial.print("\t");
Serial.println(no);
delay(1000);
}

Answer 1

Any method/filter to reduce the measured noise, and gives a steady output?
The Kalman filter is pretty much the standard method for this:
Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone.
If your background isn't maths, don't be put off by the formulas that you come across. In the single-variable case like yours, the filter is remarkably easy to implement, and I am sure googling will find a few implementations.
The filter will give you an estimate of the temperature as well an estimate of the variance of the temperature (the latter gives you an idea about how confident the filter is about its current estimate).

Answer 2

You may want to go for a simpler averaging algorithm. This is not as elegant as a low pass algorithm but may be adequate for your case. These algorithms are plentiful on the web.
You can monkey around with the number of samples you take to balance the compromise between latency and stability. You may want to start with 10 samples and work from there.